Exploring ASER2016 Pakistan Data
Yusei Hara
2020/10/1
ASER Pakistan 2016
In this piece of paper, a set of data obtained from Annual Status of Education Report (ASER) is explored. The raw data was downloaded from the link here. https://palnetwork.org/aser-centre/
Preparation
Packages Used
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(ggrepel)
library(gghighlight)
library(stringr)
library(dplyr)
library(sf)
library(scatterplot3d)
library(car)
library(ResourceSelection) #to excute Hosmer-Lemeshow test## Warning: package 'ResourceSelection' was built under R version 4.0.3# library(broom)
require(ggiraph)## Warning: package 'ggiraph' was built under R version 4.0.3require(ggiraphExtra)
# require(plyr)Data Installation
provdist <- read.csv("aser/ASER2016ProvDist.csv")
school <- read.csv("aser/ASER2016GSchool.csv")
child <- read.csv("aser/ASER2016Child.csv")
pschool <- read.csv("aser/ASER2016PvtSchool.csv")
gschool <- read.csv("aser/ASER2016GSchool.csv")
parent <- read.csv("aser/ASER2016Parent.csv")
house <- read.csv("aser/ASER2016HouseholdIndicators.csv")RegionName <- c("2" = "Panjab",
"3" = "Sindh",
"4" = "Balochistan",
"5" = "Khyber Pakhtunkhwa",
"6" = "Gilgit-Baltistan",
"7" = "Azad Jammu and Kashmir",
"8" = "Islamabad - ICT",
"9" = "Federally Administrated Tribal Areas")
Gender <- c("0" = "Male",
"-1" = "Female")Exploration
Checking Samplesizes
length(unique(child$CID))## [1] 255196The whole samplesize (the numebr of children) of this dataset is 255196.
child %>%
filter(DID == 266) %>%
summarize(N_hunza = length(unique(CID)))The samplesize of Hunza alone is 1641.
Exploration in Hunza
Gender Proportion
child %>%
filter(DID == 266) %>%
summarize(gender_proportion = mean(C002))
-1: female, 0: male
gender_proportion = -0.5173675 means there are a little more girls in the dataset.
Age of Children (C001)
child %>%
filter(DID == 266) %>%
ggplot(aes(C001)) +
geom_histogram()
Age is well sparsed
Eduation Status
child %>%
filter(DID == 266) %>%
ggplot(aes(C003)) +
geom_histogram(bins = 3)
1 = never enrolled; 2 = drop-out; 3 = currently enrolled
Education Status by Gender
child %>%
filter(DID == 266) %>%
ggplot(aes(C003)) +
geom_histogram(bins = 3, binwidth = 1) +
facet_grid(~C002, labeller = labeller(C002 = Gender))
Both genders look pretty good interms of the absolute number of currently-enrolled-children
The Enrollment Rate by Gender
child %>%
filter(DID == 266) %>%
group_by(C002) %>%
mutate(enrollment_rate = mean(C003 == 3)) %>%
ggplot(aes(C002, enrollment_rate)) +
geom_col() +
scale_y_continuous() +
geom_label(aes(label = enrollment_rate)) +
scale_x_continuous(breaks = c(-1, 0), labels = c("Female", "Male"))
As a rate, both are doing pretty good
Currently-Enrolled: Institution Type (C006) in Hunza
1 = Government; 2 = Private; 3 = Madrasah(Conventional religious education) School; 4 = Other(Non formal education facility)
child %>%
filter(DID == 266) %>%
ggplot(aes(C006)) +
geom_histogram()
Most children go to public schools or private schools
Within Gilgit-Baltistan
child %>%
filter(RID == 6) %>%
group_by(DID) %>%
mutate(Current_Enrollment_Rate = mean(C003 == 3)) %>%
ggplot(aes(DID, Current_Enrollment_Rate)) +
geom_count() +
scale_x_continuous(breaks = 260:266, labels = c("Gilgit", "Diamer", "Skardu", "Ghanshe", "Astore", "Ghizer", "Hunza-Nagar"))
Within Gilgit-Baltistan, Hunza is outperforming.
Basic Learning Levels: Reading in Local/National Language (C010)
1 = Begginer/Nothing; 2 = Letters; 3 = Words; 4 = Sentences; 5 = Story
child %>%
filter(DID == 266) %>%
ggplot(aes(C010)) +
geom_histogram()
child %>%
filter(DID == 266) %>%
summarize(na = sum(is.na(C010)))Basic Learning Levels by Age
child %>%
filter(DID == 266, C013 != c(3,4)) %>%
ggplot(aes(C010)) +
geom_histogram() +
facet_grid(~C001)
# children at he age of 3 and 4 are removed for they have not dataBasic Learning Levels by Gender
child %>%
filter(DID == 266) %>%
ggplot(aes(C010)) +
geom_histogram() +
facet_grid(~C002, labeller = labeller(C002 = Gender))
English Reading Levels (C013)
child %>%
filter(DID == 266, C013 != c(3,4)) %>%
ggplot(aes(C013)) +
geom_histogram() +
facet_grid(~C001)
# children at he age of 3 and 4 are removed for they have not dataEnglish Reading Levels by Gender
child %>%
filter(DID == 266) %>%
ggplot(aes(C013)) +
geom_histogram() +
facet_grid(~C002, labeller = labeller(C002 = Gender))
Comparison between Other Region
Current Enrollment Rate
child %>%
group_by(DID) %>%
mutate(avg = round(mean(C003 == 3), digits = 2)) %>%
ungroup() %>%
ggplot(aes(avg)) +
geom_histogram() +
facet_grid(~RID, labeller = labeller(RID = RegionName)) +
labs(title = "Current Enrollment Rate by Region")
Female Average Learning Levels (Age or other variables are NOT adjusted)
child %>%
filter(C002 == -1) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_learning, color = RID)) +
geom_point() +
geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE)
child %>%
filter(C002 == -1) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_learning, color = RID)) +
geom_point() +
geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE) +
gghighlight(RID == 6)
It is interesting to note that Gilgit-Baltistan(RID==6) has a huge diversity in average learning levels of girls and Hunza(DID==266) is in the top group of all region.
Spatial Analysis
Districts Map
ica_df <- ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
as.data.frame()## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude dataica_df <- ica_df %>% select(Province, Districts, x, y)
ica_df <- ica_df %>% summarize(Province = tolower(Province), Districts = tolower(Districts), x = x, y = y)Child and ProvDist data combination
child_dname <- child %>% left_join(provdist[-1])## Joining, by = "DID"child_dname <- child_dname %>% mutate(dname = tolower(DNAME))ica_df_3 <- ica_df %>% filter(Province == "sindh")
ica_df_3$Districts <- ica_df_3$Districts %>%
str_replace("ghotki", "gotki") %>%
str_replace("mirpur khas", "mirpurkhas") %>%
str_replace("malir karachi", "karachi-malir-rural") %>%
str_replace("naushahro feroze", "nowshero feroze") %>%
str_replace("kambar shahdad kot", "qambar shahdadkot") %>%
str_replace("sujawal", "sajawal") %>%
str_replace("shaheed benazir abad", "shaheed benazirabad") %>%
str_replace("tando allahyar", "tando allah yar") %>%
as.vector()
child_dname_3 <- child_dname %>% filter(RNAME == "Sindh") %>% left_join(ica_df_3, by = c("dname" = "Districts"))
child_dname_3 %>% group_by(dname) %>% summarize(n = sum(x))## `summarise()` ungrouping output (override with `.groups` argument)ica_df_3District IDs
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica, aes(x, y, label = DID, color = factor(RID))) +
geom_text(data = child_ica, aes(x, y, label = DID), size = 5, nudge_y = 0.2, check_overlap = TRUE)## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning: Ignoring unknown aesthetics: label## Warning: Removed 8541 rows containing missing values (geom_point).## Warning: Removed 8541 rows containing missing values (geom_text).
Gilgit-Baltistan Region
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica, aes(x, y, label = DNAME, shape = factor(RID), color = factor(RID))) +
geom_text(data = child_ica, aes(x, y, label = DNAME), size = 4.5, nudge_y = 0.2, check_overlap = TRUE) +
scale_shape_manual(values = 0:8) +
coord_sf(xlim = c(71, 77.9), ylim = c(34, 37.1))## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning: Ignoring unknown aesthetics: label## Warning: Removed 8541 rows containing missing values (geom_point).## Warning: Removed 8541 rows containing missing values (geom_text).
Latitudes and longitudes of Gilgit-Baltistan region
child_ica %>%
filter(RID == 6) %>%
summarise(xmin = min(x), xmax = max(x), ymin = min(y), ymax = max(y))Average enrollment rate
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica %>% group_by(DID) %>%
mutate(avg_enroll = mean(C003 == 3)),
aes(x, y, label = avg_enroll, color = avg_enroll))
Average enrollment rate of female children
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica %>% filter(C002 == -1) %>% group_by(DID) %>%
mutate(avg_enroll = mean(C003 == 3)),
aes(x, y, label = avg_enroll, color = avg_enroll))## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning: Ignoring unknown aesthetics: label## Warning: Removed 3566 rows containing missing values (geom_point).
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica, aes(x, y, label = C010, color = C010))
# geom_text(data = child_ica, aes(x, y, label = C010), check_overlap = TRUE, nudge_y = 1)Traial: children, gender
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica %>%
group_by(DID) %>%
mutate(gender_ratio = mean(C002)),
aes(x, y, label = gender_ratio, color = gender_ratio))
0: male, -1: female
Thus, -0.5 indicates the complete gender parity
Children
Average Local/National Language Level Across Ages by Rgion
child_ica %>%
filter(C002 == c(0,-1)) %>%
group_by(DID, C001, C002) %>%
mutate(avg_local = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_local, color = factor(C002), group = DID)) +
geom_line(show.legend = FALSE) +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender))
Average Enrollment Rate
Across Ages by Rgion
child_ica %>%
filter(C002 == c(0,-1)) %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line(show.legend = FALSE) +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender))
Hunza
child_ica %>%
filter(C002 != "NA", RID == 6, RID != "NA") %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line() +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
gghighlight(label_key = DNAME, calculate_per_facet = TRUE)
child_ica %>%
filter(C002 != "NA", RID == 6, RID != "NA") %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line() +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
gghighlight(DID == 266,label_key = DNAME, calculate_per_facet = TRUE)## Warning: Tried to calculate with group_by(), but the calculation failed.
## Falling back to ungrouped filter operation...
Karachi
child_ica %>%
filter(C002 != "NA", RID == 3) %>%
group_by(DID, C001, C002) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, color = factor(DID), group = DID)) +
geom_line() +
labs(x = "Age") +
facet_grid(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
facet_wrap(RID~C002, labeller = labeller(RID = RegionName, C002 = Gender)) +
gghighlight(DID == c(315,316), label_key = DNAME, calculate_per_facet = TRUE)
children Education Stage
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
group_by(C005) %>%
summarize(n = n()) %>%
mutate(C005 = fct_reorder(C005, n)) %>%
ggplot(aes(C005, n)) +
geom_col() +
coord_flip()## `summarise()` ungrouping output (override with `.groups` argument)
Hunza. Children Education Stage
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", !is.na(C010), C002 != "NA", !is.na(C005), DID == 266) %>%
group_by(C005) %>%
summarize(n = n()) %>%
mutate(C005 = fct_reorder(C005, n)) %>%
ggplot(aes(C005, n)) +
geom_col() +
coord_flip()## `summarise()` ungrouping output (override with `.groups` argument)
Enrollment and Gender within Gilgit-Baltistan
child_ica %>%
filter(!is.na(C002), RID == 6) %>%
group_by(DID, C001, C002) %>%
mutate(enroll = mean(C003 == 3)) %>%
ggplot(aes(C001, enroll, group = C002, color = factor(C002))) +
geom_line() +
facet_wrap(.~DID)
Education Stages
child_ica %>%
group_by(DID) %>%
mutate(n = length(C003),
dropout = sum(C003 == 2),
dropout_ratio = mean(C003 == 2),
never = sum(C003 == 1),
never_ratio = mean(C003 == 1)) %>%
ggplot(aes(DID, never_ratio, color = factor(RID))) +
geom_point()
Drop-Out C003 == 2
child_ica %>%
group_by(DID) %>%
summarize(dropout = mean(C003 == 2)) %>%
ggplot(aes(factor(DID), dropout)) +
geom_point()## `summarise()` ungrouping output (override with `.groups` argument)
child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID, C002) %>%
summarize(dropout = mean(C003 == 2)) %>%
ggplot(aes(factor(DID), dropout, color = factor(C002))) +
geom_point()## `summarise()` regrouping output by 'DID' (override with `.groups` argument)
Never Enrolled C003 == 1
child_ica %>%
group_by(DID) %>%
summarize(neverEnr = mean(C003 == 1)) %>%
ggplot(aes(factor(DID), neverEnr)) +
geom_point()## `summarise()` ungrouping output (override with `.groups` argument)
child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID, C002) %>%
summarize(neverEnr = mean(C003 == 1)) %>%
ggplot(aes(factor(DID), neverEnr, color = factor(C002))) +
geom_point()## `summarise()` regrouping output by 'DID' (override with `.groups` argument)
Hunza. Never Enrolled C003_1_01
child_ica %>%
filter(!is.na(C002)) %>%
filter(DID == 266) %>%
summarize(n_neverEnr = sum(C003 == 1),
ratio = mean(C003 == 1))child_ica %>%
filter(DID == 266, !is.na(C002)) %>%
group_by(C002) %>%
summarize(n_neverEnr = sum(C003 == 1),
ratio = mean(C003 == 1))## `summarise()` ungrouping output (override with `.groups` argument)Child & Parents
Mother Enrollment by District
child_ica %>%
filter(RID == 6, PR004 != "NA") %>%
group_by(DID, PR004) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_enrollment, fill = factor(PR004))) +
geom_col(aes(factor(DID), avg_enrollment, fill = factor(PR004)), position = "dodge")
Within Gilgit-Baltistan, Hunza is the only district where there are more women who have experience of education than those who have never enrolled in schools.
Father Enrollment by District
child_ica %>%
filter(RID == 6, PR009 != "NA") %>%
group_by(DID, PR009) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_enrollment, group = PR009, fill = factor(PR009))) +
geom_col(aes(factor(DID), avg_enrollment), position = "dodge")
Mother’s Education and Children Learning Level by Region. boxplot
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3)) %>%
ggplot(aes(PR004, avg_learning, group = PR004)) +
geom_boxplot(aes(PR004, avg_learning)) +
facet_grid(.~RID)
Father’s Education and Children Learning Level by Region. boxplot
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3)) %>%
ggplot(aes(PR009, avg_learning, group = PR009)) +
geom_boxplot(aes(PR009, avg_learning)) +
facet_grid(.~RID)
Mother’s Education and Children Learning Level by District
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), mother_enrollment = mean(PR004, na.rm = TRUE)) %>%
ggplot(aes(mother_enrollment, avg_learning)) +
geom_point()
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), mother_enrollment = mean(PR004, na.rm = TRUE)) %>%
ungroup() %>%
summarize(r = cor(mother_enrollment, avg_learning))Father’s Education and Children Learning Level by District
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3, na.rm = TRUE), father_enrollment = mean(PR009, na.rm = TRUE)) %>%
ggplot(aes(father_enrollment, avg_learning)) +
geom_point()
child_ica %>%
filter(PR009 != "NA", PR004 != "NA") %>%
group_by(DID) %>%
mutate(avg_learning = mean(C003 == 3), father_enrollment = mean(PR009)) %>%
ungroup() %>%
summarize(r = cor(father_enrollment, avg_learning))Hunza. Age, Learning Level, Mother’s Education,
child_ica %>%
filter(PR009 != "NA", PR004 != "NA", C003 != "NA", DID == 266, C001 != "NA") %>%
group_by(PR004, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR004, color = factor(PR004))) +
geom_line(aes(C001, avg_learning))## Warning: Removed 200 row(s) containing missing values (geom_path).
Hunza. Age, Learning Level, Father’s Education
child_ica %>%
filter(PR009 != "NA", PR004 != "NA", C003 != "NA", DID == 266, C001 != "NA") %>%
group_by(PR009, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR009, color = factor(PR009))) +
geom_line(aes(C001, avg_learning))## Warning: Removed 200 row(s) containing missing values (geom_path).
Gilgit-Baltistan. Age, Learning Level, Mother’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR004, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR004, color = factor(PR004))) +
geom_line(aes(C001, avg_learning)) +
facet_grid(.~DID) +
facet_wrap(.~DID)## Warning: Removed 226 row(s) containing missing values (geom_path).
Gilgit-Baltistan. Age, Learning Level, Father’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR009, C001) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_learning, group = PR009, color = factor(PR009))) +
geom_line(aes(C001, avg_learning)) +
facet_grid(.~DID) +
facet_wrap(.~DID)## Warning: Removed 226 row(s) containing missing values (geom_path).
Gilgit-Baltistan. Age, Enrollment, Mother’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR004, C001) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, group = PR004, color = factor(PR004))) +
geom_line(aes(C001, avg_enrollment)) +
facet_grid(.~DID) +
facet_wrap(.~DID)
Gilgit-Baltistan. Age, Enrollment, Father’s Education
child_ica %>%
filter(PR009 != "NA",
PR004 != "NA",
C003 != "NA",
C001 != "NA",
RID == 6) %>%
group_by(DID, PR009, C001) %>%
mutate(avg_enrollment = mean(C003 == 3, na.rm = TRUE)) %>%
ggplot(aes(C001, avg_enrollment, group = PR009, color = factor(PR009))) +
geom_line(aes(C001, avg_enrollment)) +
facet_grid(.~DID) +
facet_wrap(.~DID)
Mothers Education Level
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
ggplot(aes(PR005)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
Summary
child_ica %>%
ggplot(aes(PR006)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
Fathers Education Level
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA", C002 != "NA") %>%
ggplot(aes(PR010)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
###### Summary
child_ica %>%
ggplot(aes(PR011)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
Mothers Education Level by Region
child_ica %>%
group_by(DID, PR006) %>%
ggplot(aes(PR006)) +
geom_histogram(aes(PR006), stat = "count") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, size = 5)) +
facet_grid(.~RID)## Warning: Ignoring unknown parameters: binwidth, bins, pad
Mother Enrollment by District
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA") %>%
group_by(DID) %>%
mutate(mother_edu_rate = sum(PR004 == -1)/length(PR004)) %>%
ggplot(aes(DID, mother_edu_rate, shape = factor(RID), color = factor(RID))) +
geom_point() +
scale_shape_manual(values = 0:8)
Gilgit-Baltistan. Average Mother Education Experience, House Type
child_ica %>%
filter(PR004 != "NA", PR009 != "NA", C010 != "NA") %>%
group_by(DID) %>%
mutate(mother_edu_rate = sum(PR004 == -1)/length(PR004)) %>% ungroup() %>%
ggplot(aes(factor(RID), mother_edu_rate, group = RID)) +
geom_violin()
Hunza. Father Enroll Ratio
child_ica %>%
filter(DID == 266, !is.na(PR009)) %>%
summarize(father_edu_ratio = sum(PR009 == -1)/length(PR009))Father Enroll Ratio
child_ica %>%
filter(!is.na(PR009)) %>%
summarize(father_edu_ratio = sum(PR009 == -1)/length(PR009))Father Enroll ratio by district
child_ica %>%
filter(!is.na(PR009)) %>%
group_by(DID) %>%
mutate(father_edu_ratio = sum(PR009 == -1)/length(PR009)) %>%
ggplot(aes(factor(DID), father_edu_ratio)) +
geom_point()
mother enroll ratio by district
child_ica %>%
filter(!is.na(PR009), !is.na(PR004)) %>%
group_by(DID) %>%
mutate(mother_edu_ratio = sum(PR004 == -1)/length(PR004)) %>%
ggplot(aes(factor(DID), mother_edu_ratio)) +
geom_point()
Child, Household
Number of Children in a household
child_ica %>%
filter(!is.na(PR004), !is.na(PR009)) %>%
filter(HHID == 96546) %>%
select(CID, PRID, C002, C001, PR001, VID, C003, DID, RID)child_ica %>%
group_by(HHID) %>%
mutate(n = n()) %>%
ggplot(aes(factor(n), fill = factor(RID))) +
geom_bar(position = "dodge") +
facet_grid(.~RID) +
facet_wrap(.~RID)
house type ratio by region
child_ica %>%
ggplot(aes(x = factor(DID), y = factor(H002), fill = factor(H002))) +
geom_bar(aes(x = factor(DID), y = factor(H002)), stat = "identity", position = "fill") +
facet_grid(.~RID, scales = "free") +
facet_wrap(.~RID, scales = "free") +
theme(axis.text.x = element_text(angle = 90, size = 7, hjust = 1)) +
coord_cartesian(ylim = c(0,1), clip = "off", expand = FALSE)
Gilgit-Baltistan. house type
child_ica %>%
filter(C003 != "NA", RID == 6) %>%
group_by(DID, H002) %>%
mutate(avg_enrollment = mean(C003 == 3)) %>%
ggplot(aes(factor(DID), avg_enrollment, group = H002, fill = factor(H002))) +
geom_col(position = "dodge")
Hunza. house type
child_ica %>% left_join(house, by = "HHID") %>%
filter(DID.x == 266) %>%
ggplot(aes(H002.x)) +
geom_histogram(aes(H002.x), stat = "count")## Warning: Ignoring unknown parameters: binwidth, bins, pad
Average Number of Children in a household
child_ica %>%
group_by(HHID) %>%
mutate(n_children = length(unique(CID))) %>%
ungroup() %>%
group_by(DID) %>%
mutate(avg_n_children = mean(n_children)) %>%
ggplot(aes(DID, avg_n_children, color = factor(RID))) +
geom_point()
child_ica %>%
ggplot(aes(C005)) +
geom_histogram(stat = "count") +
coord_flip()## Warning: Ignoring unknown parameters: binwidth, bins, pad
Hunza, house type
child_ica %>%
filter(DID == 266) %>%
group_by(H002) %>%
summarize(n = n()) %>%
ggplot(aes(H002, n)) +
geom_col() +
geom_text(aes(label = n), vjust = 1.5, color = "white") +
xlab("House Type") +
ylab("Number of Households")## `summarise()` ungrouping output (override with `.groups` argument)
Hunza, number of children in households
child_ica%>%
filter(DID == 266) %>%
group_by(HHID) %>%
summarize(n = length(unique(CID))) %>%
ungroup() %>%
group_by(n) %>%
summarize(num = n()) %>%
ggplot(aes(factor(n), num)) +
geom_col() +
geom_text(aes(label = num), vjust = -0.5) +
xlab("Number of Children in Each Household") +
ylab("Number of Households") ## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
Paid-Tuition
child_ica %>%
filter(PR009 == 0, !is.na(C008a)) %>%
summarize(n = length(unique(CID)),
paid_tuition = sum(C008a == -1),
ratio = paid_tuition/n)Hunza
child_ica %>%
filter(DID == 266, PR009 == 0, !is.na(C008a)) %>%
summarize(n = length(unique(CID)),
paid_tuition = sum(C008a == -1),
ratio = paid_tuition/n)child_ica %>%
filter(DID == 266, PR004 == 0, !is.na(C008a)) %>%
summarize(n = length(unique(CID)),
paid_tuition = sum(C008a == -1),
ratio = paid_tuition/n)paid tuition by district
child_ica %>%
group_by(DID) %>%
mutate(avg_paid = mean(C008a == -1, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_paid)) +
geom_point()+
scale_y_continuous(limits = c(0,1))
child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID) %>%
summarize(avg_paid = mean(C008a == -1),
female = mean(child_ica %>% filter(C002 == -1) %>%
group_by(DID) %>% .$C003 == 3),
male = mean(child_ica %>% filter(C002 == 0) %>%
group_by(DID) %>% .$C003 == 3),
ratio = female/male)## `summarise()` ungrouping output (override with `.groups` argument)child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID, C002) %>%
summarize(enroll = mean(C003 == 3)) %>%
ungroup() %>%
spread(C002, enroll) %>%
summarize(DID = DID,
gender_ratio = .$"-1"/.$"0") %>%
ggplot(aes(DID, gender_ratio)) +
geom_point() +
scale_y_continuous(limits = c(0,1))## `summarise()` regrouping output by 'DID' (override with `.groups` argument)## Warning: Removed 16 rows containing missing values (geom_point).
correlation b/w paid-tuition and enrollment?
child_ica %>%
filter(!is.na(C002)) %>%
group_by(DID) %>%
mutate(paid_tuition = mean(C008a == -1, na.rm = TRUE)) %>%
group_by(DID, C002) %>%
mutate(enroll = mean(C003 == 3)) %>%
ggplot(aes(paid_tuition, enroll)) +
geom_point() +
geom_smooth() +
facet_grid(.~C002)## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
VID
Gilgit (DID == 260)
child_ica %>%
filter(!is.na(C002), DID == 260) %>%
group_by(VID, C002) %>%
summarize(avg_enroll = mean(C003 == 3)) %>%
ggplot(aes(VID, avg_enroll, color = factor(C002))) +
geom_point()## `summarise()` regrouping output by 'VID' (override with `.groups` argument)
Preparation for Logistic Regression Analysis
Making Dataframe With Dummy Variables
child_ica_dummy <- child_ica %>% filter(!is.na(C002), !is.na(C003), !is.na(PR004), !is.na(PR009))
child_ica_dummy$C002_01 <- ifelse(child_ica_dummy$C002 == -1, 1, 0)
child_ica_dummy$C003_01 <- ifelse(child_ica_dummy$C003 == 3, 1, 0) # currently-enrolled
child_ica_dummy$C003_1_01 <- ifelse(child_ica_dummy$C003 == 1, 1, 0) # never-enrolled
child_ica_dummy$C003_2_01 <- ifelse(child_ica_dummy$C003 == 2, 1, 0) # drop-out
child_ica_dummy$PR004_01 <- ifelse(child_ica_dummy$PR004 == -1, 1, 0)
child_ica_dummy$PR009_01 <- ifelse(child_ica_dummy$PR009 == -1, 1, 0)
child_ica_dummy$PR004_PR009_01 <- ifelse(
child_ica_dummy$PR004 == -1 | child_ica_dummy$PR009 == -1, 1, 0)
child_ica_dummy$PR004_only_01 <- ifelse(
child_ica_dummy$PR004 == -1 & child_ica_dummy$PR009 == 0, 1, 0)
child_ica_dummy$PR009_only_01 <- ifelse(
child_ica_dummy$PR004 == 0 & child_ica_dummy$PR009 == -1, 1, 0)
child_ica_dummy$PR004_PR009_both_01 <- ifelse(
child_ica_dummy$PR004 == -1 & child_ica_dummy$PR009 == -1, 1, 0)n_children_in_household <- child_ica_dummy %>%
group_by(HHID) %>%
summarize(n_children_in_household = length(unique(CID)))## `summarise()` ungrouping output (override with `.groups` argument)child_ica_dummy <- child_ica_dummy %>% left_join(n_children_in_household)## Joining, by = "HHID"child_ica_dummy$H002_1_01 <- ifelse(child_ica_dummy$H002 == 1, 1, 0)
child_ica_dummy$H002_2_01 <- ifelse(child_ica_dummy$H002 == 2, 1, 0)
child_ica_dummy$H002_3_01 <- ifelse(child_ica_dummy$H002 == 3, 1, 0)Create dummy variable with regard to region
child_ica_dummy <- child_ica_dummy
child_ica_dummy$Panjab <- ifelse(child_ica_dummy$RID == 2, 1, 0)
child_ica_dummy$Sindh <- ifelse(child_ica_dummy$RID == 3, 1, 0)
child_ica_dummy$Balochistan <- ifelse(child_ica_dummy$RID == 4, 1, 0)
child_ica_dummy$Khyber_Pakhtunkhwa <- ifelse(child_ica_dummy$RID == 5, 1, 0)
child_ica_dummy$Gilgit_Baltistan <- ifelse(child_ica_dummy$RID == 6, 1, 0)
child_ica_dummy$Azad_Jammu_and_Kashmir <- ifelse(child_ica_dummy$RID == 7, 1, 0)
child_ica_dummy$Islamabad_ICT <- ifelse(child_ica_dummy$RID == 8, 1, 0)
child_ica_dummy$Federally_Administrated_Tribal_Areas <- ifelse(child_ica_dummy$RID == 9, 1, 0)Eliminated NAs
child_ica %>%
summarize(C002_na = sum(is.na(C002)),
C003_na = sum(is.na(C003)),
PR004_na = sum(is.na(PR004)),
PR009_na = sum(is.na(PR009)))Eliminated Rows in Total
data.frame(original_rows = nrow(child_ica),
eliminated_rows = nrow(child_ica) - nrow(child_ica_dummy),
ratio = (nrow(child_ica)-nrow(child_ica_dummy))/nrow(child_ica))Eliminated NAs Hunza
child_ica %>%
filter(DID == 266) %>%
summarize(C002_na = sum(is.na(C002)),
C003_na = sum(is.na(C003)),
PR004_na = sum(is.na(PR004)),
PR009_na = sum(is.na(PR009)))Eliminated Rows in Total Hunza
data.frame(original_rows = nrow(child_ica %>% filter(DID == 266)),
eliminated_rows = nrow(child_ica %>% filter(DID == 266)) - nrow(child_ica_dummy %>% filter(DID == 266)),
ratio = (nrow(child_ica %>% filter(DID == 266) %>% filter(DID == 266))-nrow(child_ica_dummy %>% filter(DID == 266)))/nrow(child_ica %>% filter(DID == 266)))Generalized Linear Models
GLM C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8850 -1.2383 0.6563 0.8621 1.3071
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.934846 0.013780 67.84 <2e-16 ***
## n_children_in_household -0.055206 0.002935 -18.81 <2e-16 ***
## C002_01 -0.572083 0.009041 -63.28 <2e-16 ***
## PR004_PR009_01 0.711645 0.009062 78.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 289072 on 245743 degrees of freedom
## AIC: 289080
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 490.13, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5255 0.3335 0.3611 0.4126 0.6056
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.74787 0.35406 7.761 8.43e-15 ***
## n_children_in_household -0.16354 0.07071 -2.313 0.0207 *
## C002_01 0.12227 0.19347 0.632 0.5274
## PR004_PR009_01 0.44052 0.21451 2.054 0.0400 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 826.06 on 1606 degrees of freedom
## AIC: 834.06
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 9.9762, df = 8, p-value = 0.2667exponential transformation
exp(glm_child_hunza$coefficients)## (Intercept) n_children_in_household C002_01
## 15.6093953 0.8491308 1.1300567
## PR004_PR009_01
## 1.5535098confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 2.0690843 3.45821705
## n_children_in_household -0.3018419 -0.02433219
## C002_01 -0.2575389 0.50262655
## PR004_PR009_01 0.0115443 0.85445608exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 7.9175694 31.7602989
## n_children_in_household 0.7394550 0.9759614
## C002_01 0.7729515 1.6530574
## PR004_PR009_01 1.0116112 2.3500958AIC
extractAIC(glm_child_hunza)## [1] 4.0000 834.0574BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))## [1] 4.0000 855.5934effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anovavariables selection
step(glm_child_hunza_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=840.79
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 732.49 736.49
## + PR004_01 1 824.25 828.25
## + PR009_01 1 830.82 834.82
## <none> 838.79 840.79
## + C002_01 1 838.58 842.58
##
## Step: AIC=736.49
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR004_01 1 707.11 713.11
## + PR009_01 1 717.26 723.26
## <none> 732.49 736.49
## + C002_01 1 732.49 738.49
## - C001 1 838.79 840.79
##
## Step: AIC=713.11
## C003_01 ~ C001 + PR004_01
##
## Df Deviance AIC
## <none> 707.11 713.11
## + PR009_01 1 705.74 713.74
## + C002_01 1 707.11 715.11
## - PR004_01 1 732.49 736.49
## - C001 1 824.25 828.25##
## Call: glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial",
## data = child_ica_dummy %>% filter(DID == 266))
##
## Coefficients:
## (Intercept) C001 PR004_01
## -0.4789 0.3039 1.0316
##
## Degrees of Freedom: 1609 Total (i.e. Null); 1607 Residual
## Null Deviance: 838.8
## Residual Deviance: 707.1 AIC: 713.1multicolinearity
vif(glm_child_hunza)## n_children_in_household C002_01 PR004_PR009_01
## 1.087196 1.006453 1.080729GLM C003_01 ~ n_children_in_household + H002 + PR004_PR009_01
glm_child <- glm(C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.927 -1.297 0.723 0.869 1.186
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.094686 0.016614 5.699 1.2e-08 ***
## n_children_in_household -0.049487 0.002933 -16.871 < 2e-16 ***
## H002 0.379659 0.006366 59.636 < 2e-16 ***
## PR004_PR009_01 0.502871 0.009477 53.061 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 289446 on 245743 degrees of freedom
## AIC: 289454
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + H002 + PR004_PR009_01
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + H002 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6103 0.3376 0.3660 0.4038 0.5903
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.23266 0.49678 6.507 7.65e-11 ***
## n_children_in_household -0.16663 0.07002 -2.380 0.0173 *
## H002 -0.20362 0.16445 -1.238 0.2157
## PR004_PR009_01 0.51067 0.22107 2.310 0.0209 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 824.91 on 1606 degrees of freedom
## AIC: 832.91
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 15.469, df = 8, p-value = 0.05064exponential transformation
exp(glm_child_hunza$coefficients)## (Intercept) n_children_in_household H002
## 25.3469307 0.8465115 0.8157746
## PR004_PR009_01
## 1.6664083confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 2.27327932 4.22193092
## n_children_in_household -0.30339827 -0.02854365
## H002 -0.52934555 0.11610748
## PR004_PR009_01 0.06943756 0.93802587exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 9.7111948 68.1649785
## n_children_in_household 0.7383050 0.9718599
## H002 0.5889903 1.1231166
## PR004_PR009_01 1.0719051 2.5549327AIC
extractAIC(glm_child_hunza)## [1] 4.0000 832.9071BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))## [1] 4.0000 854.4431effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anovavariables selection
step(glm_child_hunza_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=840.79
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 732.49 736.49
## + PR004_01 1 824.25 828.25
## + PR009_01 1 830.82 834.82
## <none> 838.79 840.79
## + C002_01 1 838.58 842.58
##
## Step: AIC=736.49
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR004_01 1 707.11 713.11
## + PR009_01 1 717.26 723.26
## <none> 732.49 736.49
## + C002_01 1 732.49 738.49
## - C001 1 838.79 840.79
##
## Step: AIC=713.11
## C003_01 ~ C001 + PR004_01
##
## Df Deviance AIC
## <none> 707.11 713.11
## + PR009_01 1 705.74 713.74
## + C002_01 1 707.11 715.11
## - PR004_01 1 732.49 736.49
## - C001 1 824.25 828.25##
## Call: glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial",
## data = child_ica_dummy %>% filter(DID == 266))
##
## Coefficients:
## (Intercept) C001 PR004_01
## -0.4789 0.3039 1.0316
##
## Degrees of Freedom: 1609 Total (i.e. Null); 1607 Residual
## Null Deviance: 838.8
## Residual Deviance: 707.1 AIC: 713.1# step(glm_child_null, direction = "both",
# scope = (~ C001 + C002 + PR004_01 + PR009_01 +
# Panjab + Sindh + Balochistan + Khyber_Pakhtunkhwa + Gilgit_Baltistan +
# Azad_Jammu_and_Kashmir + Islamabad_ICT))multicolinearity
vif(glm_child_hunza)## n_children_in_household H002 PR004_PR009_01
## 1.083416 1.088995 1.147110GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + H002
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + H002, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## H002, family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.927 -1.297 0.723 0.869 1.186
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.094686 0.016614 5.699 1.2e-08 ***
## n_children_in_household -0.049487 0.002933 -16.871 < 2e-16 ***
## PR004_PR009_01 0.502871 0.009477 53.061 < 2e-16 ***
## H002 0.379659 0.006366 59.636 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 289446 on 245743 degrees of freedom
## AIC: 289454
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + PR004_PR009_01 + H002
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## H002, family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6103 0.3376 0.3660 0.4038 0.5903
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.23266 0.49678 6.507 7.65e-11 ***
## n_children_in_household -0.16663 0.07002 -2.380 0.0173 *
## PR004_PR009_01 0.51067 0.22107 2.310 0.0209 *
## H002 -0.20362 0.16445 -1.238 0.2157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 824.91 on 1606 degrees of freedom
## AIC: 832.91
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 355.01, df = 8, p-value < 2.2e-16Multicolinearity house type and parent edu?
child_ica_dummy %>%
group_by(H002) %>%
mutate(n_parents = length(PR004_PR009_01))%>%
ggplot(aes(H002, n_parents, fill = factor(PR004_PR009_01))) +
geom_col(position = "stack") +
xlab("Type of Houses") +
ylab("Number of Parents") +
labs(fill = "Parents Education\n(at least one of them, primary or more)")
The more educated the parents are, the better house they are living in.
GLM C003_01 ~ n_children_in_household + H002 + C002_01
glm_child <- glm(C003_01 ~ n_children_in_household + H002 + C002_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0122 -1.2391 0.7184 0.8909 1.2724
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.424111 0.016894 25.10 <2e-16 ***
## n_children_in_household -0.052085 0.002941 -17.71 <2e-16 ***
## H002 0.503563 0.006129 82.16 <2e-16 ***
## C002_01 -0.575354 0.009055 -63.54 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 288199 on 245743 degrees of freedom
## AIC: 288207
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 818.15, df = 8, p-value < 2.2e-16Hunza. C003_01 ~ n_children_in_household + H002 + C002_01
glm_child_hunza <- glm(C003_01 ~ n_children_in_household + H002 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5804 0.3373 0.3731 0.4124 0.5843
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.48657 0.48393 7.205 5.82e-13 ***
## n_children_in_household -0.20822 0.06887 -3.023 0.0025 **
## H002 -0.11589 0.15847 -0.731 0.4646
## C002_01 0.13022 0.19320 0.674 0.5003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 829.57 on 1606 degrees of freedom
## AIC: 837.57
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 12.015, df = 8, p-value = 0.1505exponential transformation
exp(glm_child_hunza$coefficients)## (Intercept) n_children_in_household H002
## 32.6735833 0.8120302 0.8905720
## C002_01
## 1.1390753confidence interval (intercept and coefficient)
confint(glm_child_hunza, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 2.5525708 4.45124970
## n_children_in_household -0.3424699 -0.07212852
## H002 -0.4291928 0.19275626
## C002_01 -0.2490334 0.51006070exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child_hunza, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 12.8400705 85.7340185
## n_children_in_household 0.7100145 0.9304113
## H002 0.6510344 1.2125872
## C002_01 0.7795540 1.6653923AIC
extractAIC(glm_child_hunza)## [1] 4.0000 837.5663BIC
extractAIC(glm_child_hunza, k = log(nrow(glm_child_hunza$data)))## [1] 4.0000 859.1022effectiveness of explanatory variables
glm_child_hunza_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
anova <- anova(glm_child_hunza_null, glm_child_hunza, test = "Chisq")
anovavariables selection
step(glm_child_hunza_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=840.79
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 732.49 736.49
## + PR004_01 1 824.25 828.25
## + PR009_01 1 830.82 834.82
## <none> 838.79 840.79
## + C002_01 1 838.58 842.58
##
## Step: AIC=736.49
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR004_01 1 707.11 713.11
## + PR009_01 1 717.26 723.26
## <none> 732.49 736.49
## + C002_01 1 732.49 738.49
## - C001 1 838.79 840.79
##
## Step: AIC=713.11
## C003_01 ~ C001 + PR004_01
##
## Df Deviance AIC
## <none> 707.11 713.11
## + PR009_01 1 705.74 713.74
## + C002_01 1 707.11 715.11
## - PR004_01 1 732.49 736.49
## - C001 1 824.25 828.25##
## Call: glm(formula = C003_01 ~ C001 + PR004_01, family = "binomial",
## data = child_ica_dummy %>% filter(DID == 266))
##
## Coefficients:
## (Intercept) C001 PR004_01
## -0.4789 0.3039 1.0316
##
## Degrees of Freedom: 1609 Total (i.e. Null); 1607 Residual
## Null Deviance: 838.8
## Residual Deviance: 707.1 AIC: 713.1# step(glm_child_null, direction = "both",
# scope = (~ C001 + C002 + PR004_01 + PR009_01 +
# Panjab + Sindh + Balochistan + Khyber_Pakhtunkhwa + Gilgit_Baltistan +
# Azad_Jammu_and_Kashmir + Islamabad_ICT))multicolinearity
vif(glm_child_hunza)## n_children_in_household H002 C002_01
## 1.021627 1.015482 1.006245GLM C003_01 ~ n_children_in_household + C002_01 + H002
glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + H002, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002,
## family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0122 -1.2391 0.7184 0.8909 1.2724
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.424111 0.016894 25.10 <2e-16 ***
## n_children_in_household -0.052085 0.002941 -17.71 <2e-16 ***
## C002_01 -0.575354 0.009055 -63.54 <2e-16 ***
## H002 0.503563 0.006129 82.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 288199 on 245743 degrees of freedom
## AIC: 288207
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 818.15, df = 8, p-value < 2.2e-16Hunza. C003_01 ~n_children_in_household + C002_01 + H002
glm_child_hunza <- glm(C003_01 ~n_children_in_household + C002_01 + H002, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child_hunza, se = TRUE, show.summary = TRUE, point = TRUE, colorAsFactor = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002,
## family = "binomial", data = child_ica_dummy %>% filter(DID ==
## 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5804 0.3373 0.3731 0.4124 0.5843
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.48657 0.48393 7.205 5.82e-13 ***
## n_children_in_household -0.20822 0.06887 -3.023 0.0025 **
## C002_01 0.13022 0.19320 0.674 0.5003
## H002 -0.11589 0.15847 -0.731 0.4646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 838.79 on 1609 degrees of freedom
## Residual deviance: 829.57 on 1606 degrees of freedom
## AIC: 837.57
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child_hunza$y, y = fitted(glm_child_hunza))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child_hunza$y, fitted(glm_child_hunza)
## X-squared = 12.015, df = 8, p-value = 0.1505C002_01 chi Districts in Gilgit-Baltistan
child_ica_dummy %>% filter(RID == 6) %>% summarize(unique(DID))sapply(260:266, function(dist){
glm_child_dist <- glm(C003_01 ~ C002_01, family = "binomial", data = child_ica_dummy %>% filter(RID == 6, DID == dist))
glm_child_dist_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy %>% filter(RID == 6, DID == dist))
anova <- anova(glm_child_dist_null, glm_child_dist, test = "Chisq")
data.frame(result = anova$`Pr(>Chi)`[2], DID = dist, chi_0.05 = anova$`Pr(>Chi)`[2] >= 0.05)
})## [,1] [,2] [,3] [,4] [,5] [,6]
## result 0.02323511 1.711351e-125 0.02518717 0.04226537 0.1507538 0.7348869
## DID 260 261 262 263 264 265
## chi_0.05 FALSE FALSE FALSE FALSE TRUE TRUE
## [,7]
## result 0.6463022
## DID 266
## chi_0.05 TRUE
Within Gilgit-Baltistan, 3 of 7 districts including Hunza has Chi > 0.05
GLM C003_01 ~ C002_01 and Anova for All Districts
DID_unique <- child_ica_dummy %>% summarize(unique(DID))
DID_unique <- as.matrix(DID_unique)
DID_unique <- as.numeric(DID_unique[,1])
dist_logist <- sapply(DID_unique, function(dist){
glm_child_dist <- glm(C003_01 ~ C002_01,
family = "binomial",
data = child_ica_dummy %>%
filter(DID == dist))
glm_child_dist_null <- glm(C003_01~1,
family = "binomial",
data = child_ica_dummy %>%
filter(DID == dist))
anova <- anova(glm_child_dist_null, glm_child_dist, test = "Chisq")
data.frame(DID = dist,
chi = anova$`Pr(>Chi)`[2],
chi_largerThan_0.05 = anova$`Pr(>Chi)`[2] >= 0.05,
chi_largerThan_0.1 = anova$`Pr(>Chi)`[2] >= 0.1)
})
anova_dist <- as.data.frame(as.tibble(t(dist_logist)))## Warning: `as.tibble()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.anova_dist$DID <- as.numeric(anova_dist$DID)
anova_dist$chi <- as.numeric(anova_dist$chi)
anova_dist$chi_largerThan_0.1 <- as.logical(anova_dist$chi_largerThan_0.1)
anova_dist$chi_largerThan_0.05 <- as.logical(anova_dist$chi_largerThan_0.05)anova_dist %>%
summarize(total_chi_largerThan_0.05 = sum(as.numeric(chi_largerThan_0.05)),
ratio_0.05 = sum(as.numeric(chi_largerThan_0.05))/length(chi_largerThan_0.05),
total_chi_largerThan_0.1 = sum(as.numeric(chi_largerThan_0.1)),
ratio_0.1 = sum(as.numeric(chi_largerThan_0.1))/length(chi_largerThan_0.1))DIDs with chi >= 0.05
anova_dist %>%
filter(chi_largerThan_0.05 == TRUE) %>%
select(-chi_largerThan_0.1)DID chi > 0.05
DID_chi_0.05 <- anova_dist %>%
filter(chi_largerThan_0.05 == TRUE) %>%
select(DID) %>%
pull()
DID_chi_0.05## [1] 146 151 156 158 159 162 163 164 167 171 173 176 178 179 189 196 245 257 264
## [20] 265 266 267 268 269 270 271 272 273 274 276DIDs with chi >= 0.1
anova_dist %>%
filter(chi_largerThan_0.1 == TRUE) %>%
select(-chi_largerThan_0.05)DID chi > 0.1
DID_chi_0.1 <- anova_dist %>%
filter(chi_largerThan_0.1 == TRUE) %>%
select(DID) %>%
pull()DID with C002 == -1, chi > 0.05
child %>%
filter(C002 == -1) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)) %>%
ggplot(aes(DID, avg_learning)) +
geom_point(data = child %>%
filter(C002 == -1 & DID == DID_chi_0.05) %>%
group_by(DID) %>%
mutate(avg_learning = mean(C010, na.rm = TRUE)), size = 4, color = "red") +
geom_point(aes(color = RID)) +
geom_text(aes(label = DID), nudge_x = 5, check_overlap = TRUE)## Warning in DID == DID_chi_0.05: 長いオブジェクトの長さが短いオブジェクトの長さの
## 倍数になっていません
C001 & enroll ; DID chi > 0.1
child %>%
filter(DID == DID_chi_0.1) %>%
group_by(DID, C001) %>%
mutate(enroll = mean(C003 == 3)) %>%
ggplot(aes(C001, enroll)) +
geom_point() +
geom_smooth() +
facet_wrap(DID~.)## Warning in DID == DID_chi_0.1: 長いオブジェクトの長さが短いオブジェクトの長さの
## 倍数になっていません## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
GLM C003_01 ~ n_children_in_household + H002 + C002_01 + as.factor(DID)
glm_child <- glm(C003_01 ~ n_children_in_household + H002 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + H002 + C002_01 +
## as.factor(DID), family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5433 -1.0857 0.6008 0.8455 1.6963
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.856387 0.090069 20.611 < 2e-16 ***
## n_children_in_household -0.030048 0.003248 -9.252 < 2e-16 ***
## H002 0.238646 0.007614 31.342 < 2e-16 ***
## C002_01 -0.661321 0.009514 -69.509 < 2e-16 ***
## as.factor(DID)147 -0.531421 0.106816 -4.975 6.52e-07 ***
## as.factor(DID)148 -0.080366 0.111073 -0.724 0.469342
## as.factor(DID)149 -0.729406 0.102725 -7.101 1.24e-12 ***
## as.factor(DID)150 -0.755588 0.101817 -7.421 1.16e-13 ***
## as.factor(DID)151 -0.188233 0.111184 -1.693 0.090458 .
## as.factor(DID)152 -0.294774 0.115283 -2.557 0.010559 *
## as.factor(DID)153 -0.876057 0.103921 -8.430 < 2e-16 ***
## as.factor(DID)154 -0.635280 0.105517 -6.021 1.74e-09 ***
## as.factor(DID)155 -0.824648 0.101025 -8.163 3.27e-16 ***
## as.factor(DID)156 -1.295679 0.115699 -11.199 < 2e-16 ***
## as.factor(DID)157 -0.457369 0.110809 -4.128 3.67e-05 ***
## as.factor(DID)158 -0.353198 0.107855 -3.275 0.001058 **
## as.factor(DID)159 -1.312255 0.106407 -12.332 < 2e-16 ***
## as.factor(DID)160 -0.699375 0.104766 -6.676 2.46e-11 ***
## as.factor(DID)161 -1.308519 0.097458 -13.426 < 2e-16 ***
## as.factor(DID)162 0.210991 0.125085 1.687 0.091645 .
## as.factor(DID)163 0.071172 0.127802 0.557 0.577599
## as.factor(DID)164 -0.508366 0.113661 -4.473 7.73e-06 ***
## as.factor(DID)165 -0.991673 0.104159 -9.521 < 2e-16 ***
## as.factor(DID)166 -0.869402 0.104533 -8.317 < 2e-16 ***
## as.factor(DID)167 0.526340 0.130238 4.041 5.31e-05 ***
## as.factor(DID)169 -1.655333 0.099225 -16.683 < 2e-16 ***
## as.factor(DID)170 -0.488745 0.108583 -4.501 6.76e-06 ***
## as.factor(DID)171 -0.436012 0.110567 -3.943 8.03e-05 ***
## as.factor(DID)172 -0.624901 0.106396 -5.873 4.27e-09 ***
## as.factor(DID)173 -0.306239 0.109770 -2.790 0.005274 **
## as.factor(DID)174 -0.895481 0.105491 -8.489 < 2e-16 ***
## as.factor(DID)175 -1.320879 0.101365 -13.031 < 2e-16 ***
## as.factor(DID)176 -0.464176 0.114670 -4.048 5.17e-05 ***
## as.factor(DID)177 -0.853066 0.102982 -8.284 < 2e-16 ***
## as.factor(DID)178 0.182187 0.117181 1.555 0.120006
## as.factor(DID)179 -0.718075 0.105393 -6.813 9.54e-12 ***
## as.factor(DID)180 -0.673160 0.105975 -6.352 2.12e-10 ***
## as.factor(DID)181 -0.158811 0.115371 -1.377 0.168658
## as.factor(DID)182 -1.252132 0.100397 -12.472 < 2e-16 ***
## as.factor(DID)183 -0.624218 0.107630 -5.800 6.64e-09 ***
## as.factor(DID)184 -1.279621 0.099900 -12.809 < 2e-16 ***
## as.factor(DID)185 0.315727 0.116482 2.711 0.006718 **
## as.factor(DID)186 -1.527947 0.099616 -15.338 < 2e-16 ***
## as.factor(DID)187 -1.365833 0.101106 -13.509 < 2e-16 ***
## as.factor(DID)188 -1.201238 0.100289 -11.978 < 2e-16 ***
## as.factor(DID)189 -0.279213 0.108191 -2.581 0.009858 **
## as.factor(DID)190 -0.984810 0.108473 -9.079 < 2e-16 ***
## as.factor(DID)191 -0.856359 0.108294 -7.908 2.62e-15 ***
## as.factor(DID)192 -0.907494 0.102627 -8.843 < 2e-16 ***
## as.factor(DID)193 -0.062637 0.110415 -0.567 0.570519
## as.factor(DID)194 -1.641845 0.099810 -16.450 < 2e-16 ***
## as.factor(DID)195 -1.800501 0.100992 -17.828 < 2e-16 ***
## as.factor(DID)196 -0.643909 0.109777 -5.866 4.47e-09 ***
## as.factor(DID)197 -1.333062 0.100752 -13.231 < 2e-16 ***
## as.factor(DID)198 -1.750612 0.099615 -17.574 < 2e-16 ***
## as.factor(DID)199 0.411202 0.116763 3.522 0.000429 ***
## as.factor(DID)200 -1.504418 0.102715 -14.647 < 2e-16 ***
## as.factor(DID)202 -1.430222 0.100851 -14.182 < 2e-16 ***
## as.factor(DID)203 -1.244656 0.099211 -12.546 < 2e-16 ***
## as.factor(DID)204 -0.864444 0.103348 -8.364 < 2e-16 ***
## as.factor(DID)205 -1.475565 0.098277 -15.014 < 2e-16 ***
## as.factor(DID)206 -1.502885 0.098520 -15.255 < 2e-16 ***
## as.factor(DID)207 -1.931182 0.100015 -19.309 < 2e-16 ***
## as.factor(DID)208 -1.433055 0.099404 -14.417 < 2e-16 ***
## as.factor(DID)209 -0.835136 0.108254 -7.715 1.21e-14 ***
## as.factor(DID)210 -1.656130 0.097887 -16.919 < 2e-16 ***
## as.factor(DID)211 -0.940851 0.107889 -8.721 < 2e-16 ***
## as.factor(DID)212 -1.184811 0.102333 -11.578 < 2e-16 ***
## as.factor(DID)213 -1.492092 0.098622 -15.129 < 2e-16 ***
## as.factor(DID)214 -1.980250 0.099212 -19.960 < 2e-16 ***
## as.factor(DID)215 -1.277002 0.096980 -13.168 < 2e-16 ***
## as.factor(DID)216 -1.475047 0.101753 -14.496 < 2e-16 ***
## as.factor(DID)217 -1.645835 0.099970 -16.463 < 2e-16 ***
## as.factor(DID)218 -1.391871 0.097592 -14.262 < 2e-16 ***
## as.factor(DID)219 -1.414559 0.100711 -14.046 < 2e-16 ***
## as.factor(DID)220 -2.067994 0.096336 -21.466 < 2e-16 ***
## as.factor(DID)221 -2.314209 0.101510 -22.798 < 2e-16 ***
## as.factor(DID)222 -1.569588 0.097845 -16.042 < 2e-16 ***
## as.factor(DID)223 -1.497175 0.105467 -14.196 < 2e-16 ***
## as.factor(DID)224 -2.024431 0.099120 -20.424 < 2e-16 ***
## as.factor(DID)225 -1.626735 0.099947 -16.276 < 2e-16 ***
## as.factor(DID)226 -1.380602 0.102822 -13.427 < 2e-16 ***
## as.factor(DID)227 -1.587866 0.098118 -16.183 < 2e-16 ***
## as.factor(DID)228 -2.149597 0.103253 -20.819 < 2e-16 ***
## as.factor(DID)229 -2.016092 0.101742 -19.816 < 2e-16 ***
## as.factor(DID)230 -1.952694 0.099633 -19.599 < 2e-16 ***
## as.factor(DID)231 -1.323551 0.098863 -13.388 < 2e-16 ***
## as.factor(DID)232 -0.149362 0.107653 -1.387 0.165307
## as.factor(DID)233 -2.172729 0.104344 -20.823 < 2e-16 ***
## as.factor(DID)234 -1.481327 0.105267 -14.072 < 2e-16 ***
## as.factor(DID)235 -0.728578 0.102880 -7.082 1.42e-12 ***
## as.factor(DID)236 -1.011111 0.103059 -9.811 < 2e-16 ***
## as.factor(DID)237 -0.768246 0.105928 -7.253 4.09e-13 ***
## as.factor(DID)238 0.092540 0.116242 0.796 0.425973
## as.factor(DID)239 -0.895697 0.102414 -8.746 < 2e-16 ***
## as.factor(DID)240 -1.139789 0.102174 -11.155 < 2e-16 ***
## as.factor(DID)241 -2.331117 0.098219 -23.734 < 2e-16 ***
## as.factor(DID)242 -1.090313 0.103367 -10.548 < 2e-16 ***
## as.factor(DID)243 -1.092509 0.108519 -10.067 < 2e-16 ***
## as.factor(DID)244 -0.389501 0.110870 -3.513 0.000443 ***
## as.factor(DID)245 0.307911 0.125277 2.458 0.013978 *
## as.factor(DID)246 -1.693232 0.100045 -16.925 < 2e-16 ***
## as.factor(DID)247 -1.233913 0.107645 -11.463 < 2e-16 ***
## as.factor(DID)248 -0.789366 0.100028 -7.891 2.99e-15 ***
## as.factor(DID)249 -0.023336 0.124757 -0.187 0.851620
## as.factor(DID)250 -0.687107 0.105155 -6.534 6.39e-11 ***
## as.factor(DID)251 -0.540857 0.106874 -5.061 4.18e-07 ***
## as.factor(DID)252 -0.860171 0.103757 -8.290 < 2e-16 ***
## as.factor(DID)253 -0.422480 0.111991 -3.772 0.000162 ***
## as.factor(DID)254 -1.716154 0.115165 -14.902 < 2e-16 ***
## as.factor(DID)255 -0.718627 0.108954 -6.596 4.23e-11 ***
## as.factor(DID)256 -0.192197 0.106990 -1.796 0.072430 .
## as.factor(DID)257 0.525470 0.140459 3.741 0.000183 ***
## as.factor(DID)258 -1.393632 0.099084 -14.065 < 2e-16 ***
## as.factor(DID)259 -0.803014 0.107179 -7.492 6.77e-14 ***
## as.factor(DID)260 -0.030875 0.108454 -0.285 0.775887
## as.factor(DID)261 -2.203777 0.098347 -22.408 < 2e-16 ***
## as.factor(DID)262 -0.562816 0.103736 -5.425 5.78e-08 ***
## as.factor(DID)263 -0.168283 0.107682 -1.563 0.118105
## as.factor(DID)264 -0.378301 0.105640 -3.581 0.000342 ***
## as.factor(DID)265 0.045178 0.114401 0.395 0.692909
## as.factor(DID)266 0.651750 0.130083 5.010 5.44e-07 ***
## as.factor(DID)267 -0.138255 0.106774 -1.295 0.195374
## as.factor(DID)268 -0.998930 0.104179 -9.589 < 2e-16 ***
## as.factor(DID)269 0.089197 0.112064 0.796 0.426058
## as.factor(DID)270 0.047179 0.111679 0.422 0.672695
## as.factor(DID)271 0.316533 0.115617 2.738 0.006186 **
## as.factor(DID)272 0.145384 0.112480 1.293 0.196175
## as.factor(DID)273 -0.121213 0.109283 -1.109 0.267356
## as.factor(DID)274 0.006667 0.120782 0.055 0.955983
## as.factor(DID)275 -1.031188 0.101880 -10.122 < 2e-16 ***
## as.factor(DID)276 -0.061414 0.106435 -0.577 0.563931
## as.factor(DID)277 -0.253596 0.146904 -1.726 0.084300 .
## as.factor(DID)278 -0.665286 0.098548 -6.751 1.47e-11 ***
## as.factor(DID)279 -1.754758 0.100067 -17.536 < 2e-16 ***
## as.factor(DID)280 -0.906296 0.102311 -8.858 < 2e-16 ***
## as.factor(DID)281 -0.667260 0.101980 -6.543 6.03e-11 ***
## as.factor(DID)282 -0.588057 0.109290 -5.381 7.42e-08 ***
## as.factor(DID)284 -0.676702 0.104745 -6.460 1.04e-10 ***
## as.factor(DID)287 -0.907293 0.104730 -8.663 < 2e-16 ***
## as.factor(DID)289 -0.917523 0.101097 -9.076 < 2e-16 ***
## as.factor(DID)290 -0.888790 0.104415 -8.512 < 2e-16 ***
## as.factor(DID)315 -0.736484 0.115424 -6.381 1.76e-10 ***
## as.factor(DID)316 -1.195621 0.103364 -11.567 < 2e-16 ***
## as.factor(DID)318 -1.593066 0.105466 -15.105 < 2e-16 ***
## as.factor(DID)319 -2.132163 0.099025 -21.532 < 2e-16 ***
## as.factor(DID)320 -1.398009 0.104773 -13.343 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 269755 on 245600 degrees of freedom
## AIC: 270049
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 330.28, df = 8, p-value < 2.2e-16GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5059 -1.0668 0.6014 0.8352 1.7174
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.161512 0.088576 24.403 < 2e-16 ***
## n_children_in_household -0.028508 0.003251 -8.768 < 2e-16 ***
## PR004_PR009_01 0.347071 0.010500 33.054 < 2e-16 ***
## C002_01 -0.664193 0.009519 -69.776 < 2e-16 ***
## as.factor(DID)147 -0.607598 0.106738 -5.692 1.25e-08 ***
## as.factor(DID)148 -0.093994 0.111048 -0.846 0.397314
## as.factor(DID)149 -0.804572 0.102678 -7.836 4.65e-15 ***
## as.factor(DID)150 -0.784029 0.101828 -7.700 1.37e-14 ***
## as.factor(DID)151 -0.190780 0.111171 -1.716 0.086146 .
## as.factor(DID)152 -0.334471 0.115245 -2.902 0.003705 **
## as.factor(DID)153 -0.900790 0.103870 -8.672 < 2e-16 ***
## as.factor(DID)154 -0.678256 0.105494 -6.429 1.28e-10 ***
## as.factor(DID)155 -0.842393 0.101011 -8.340 < 2e-16 ***
## as.factor(DID)156 -1.313467 0.115592 -11.363 < 2e-16 ***
## as.factor(DID)157 -0.452119 0.110799 -4.081 4.49e-05 ***
## as.factor(DID)158 -0.343909 0.107871 -3.188 0.001432 **
## as.factor(DID)159 -1.309531 0.106342 -12.314 < 2e-16 ***
## as.factor(DID)160 -0.738367 0.104702 -7.052 1.76e-12 ***
## as.factor(DID)161 -1.277624 0.097450 -13.111 < 2e-16 ***
## as.factor(DID)162 0.140673 0.125046 1.125 0.260600
## as.factor(DID)163 0.076054 0.127784 0.595 0.551728
## as.factor(DID)164 -0.566415 0.113648 -4.984 6.23e-07 ***
## as.factor(DID)165 -0.978953 0.104104 -9.404 < 2e-16 ***
## as.factor(DID)166 -0.830884 0.104540 -7.948 1.90e-15 ***
## as.factor(DID)167 0.509703 0.130242 3.914 9.10e-05 ***
## as.factor(DID)169 -1.723715 0.099078 -17.398 < 2e-16 ***
## as.factor(DID)170 -0.472852 0.108538 -4.357 1.32e-05 ***
## as.factor(DID)171 -0.437550 0.110541 -3.958 7.55e-05 ***
## as.factor(DID)172 -0.638525 0.106358 -6.004 1.93e-09 ***
## as.factor(DID)173 -0.267159 0.109787 -2.433 0.014957 *
## as.factor(DID)174 -0.956716 0.105408 -9.076 < 2e-16 ***
## as.factor(DID)175 -1.331763 0.101311 -13.145 < 2e-16 ***
## as.factor(DID)176 -0.472402 0.114611 -4.122 3.76e-05 ***
## as.factor(DID)177 -0.852129 0.102954 -8.277 < 2e-16 ***
## as.factor(DID)178 0.202014 0.117141 1.725 0.084612 .
## as.factor(DID)179 -0.718205 0.105390 -6.815 9.44e-12 ***
## as.factor(DID)180 -0.678592 0.105962 -6.404 1.51e-10 ***
## as.factor(DID)181 -0.117571 0.115344 -1.019 0.308054
## as.factor(DID)182 -1.388829 0.100242 -13.855 < 2e-16 ***
## as.factor(DID)183 -0.659636 0.107584 -6.131 8.71e-10 ***
## as.factor(DID)184 -1.307346 0.099855 -13.092 < 2e-16 ***
## as.factor(DID)185 0.289557 0.116541 2.485 0.012970 *
## as.factor(DID)186 -1.693746 0.099333 -17.051 < 2e-16 ***
## as.factor(DID)187 -1.457246 0.100967 -14.433 < 2e-16 ***
## as.factor(DID)188 -1.301699 0.100182 -12.993 < 2e-16 ***
## as.factor(DID)189 -0.357912 0.108160 -3.309 0.000936 ***
## as.factor(DID)190 -0.994167 0.108433 -9.168 < 2e-16 ***
## as.factor(DID)191 -0.905377 0.108243 -8.364 < 2e-16 ***
## as.factor(DID)192 -1.059675 0.102504 -10.338 < 2e-16 ***
## as.factor(DID)193 -0.060710 0.110471 -0.550 0.582622
## as.factor(DID)194 -1.683985 0.099732 -16.885 < 2e-16 ***
## as.factor(DID)195 -1.882723 0.100866 -18.666 < 2e-16 ***
## as.factor(DID)196 -0.698907 0.109777 -6.367 1.93e-10 ***
## as.factor(DID)197 -1.429311 0.100649 -14.201 < 2e-16 ***
## as.factor(DID)198 -1.785280 0.099553 -17.933 < 2e-16 ***
## as.factor(DID)199 0.351822 0.116662 3.016 0.002564 **
## as.factor(DID)200 -1.457248 0.102727 -14.186 < 2e-16 ***
## as.factor(DID)202 -1.476437 0.100767 -14.652 < 2e-16 ***
## as.factor(DID)203 -1.442589 0.099023 -14.568 < 2e-16 ***
## as.factor(DID)204 -0.894943 0.103239 -8.669 < 2e-16 ***
## as.factor(DID)205 -1.587176 0.098221 -16.159 < 2e-16 ***
## as.factor(DID)206 -1.652157 0.098176 -16.828 < 2e-16 ***
## as.factor(DID)207 -2.029097 0.099744 -20.343 < 2e-16 ***
## as.factor(DID)208 -1.513908 0.099299 -15.246 < 2e-16 ***
## as.factor(DID)209 -1.027678 0.107955 -9.519 < 2e-16 ***
## as.factor(DID)210 -1.723325 0.097692 -17.640 < 2e-16 ***
## as.factor(DID)211 -1.029961 0.107737 -9.560 < 2e-16 ***
## as.factor(DID)212 -1.344713 0.102072 -13.174 < 2e-16 ***
## as.factor(DID)213 -1.582224 0.098485 -16.066 < 2e-16 ***
## as.factor(DID)214 -2.095527 0.099005 -21.166 < 2e-16 ***
## as.factor(DID)215 -1.359969 0.096766 -14.054 < 2e-16 ***
## as.factor(DID)216 -1.675878 0.101392 -16.529 < 2e-16 ***
## as.factor(DID)217 -1.709895 0.099764 -17.139 < 2e-16 ***
## as.factor(DID)218 -1.634273 0.097160 -16.820 < 2e-16 ***
## as.factor(DID)219 -1.542831 0.100494 -15.352 < 2e-16 ***
## as.factor(DID)220 -2.103022 0.096242 -21.851 < 2e-16 ***
## as.factor(DID)221 -2.455716 0.101216 -24.262 < 2e-16 ***
## as.factor(DID)222 -1.692441 0.097663 -17.329 < 2e-16 ***
## as.factor(DID)223 -1.604057 0.105314 -15.231 < 2e-16 ***
## as.factor(DID)224 -2.148013 0.098930 -21.712 < 2e-16 ***
## as.factor(DID)225 -1.852791 0.099548 -18.612 < 2e-16 ***
## as.factor(DID)226 -1.388128 0.102732 -13.512 < 2e-16 ***
## as.factor(DID)227 -1.677606 0.097855 -17.144 < 2e-16 ***
## as.factor(DID)228 -2.257037 0.102988 -21.916 < 2e-16 ***
## as.factor(DID)229 -1.971458 0.101725 -19.380 < 2e-16 ***
## as.factor(DID)230 -2.137518 0.099303 -21.525 < 2e-16 ***
## as.factor(DID)231 -1.403955 0.098699 -14.225 < 2e-16 ***
## as.factor(DID)232 -0.293014 0.107407 -2.728 0.006370 **
## as.factor(DID)233 -2.270591 0.104146 -21.802 < 2e-16 ***
## as.factor(DID)234 -1.583978 0.105021 -15.082 < 2e-16 ***
## as.factor(DID)235 -0.832266 0.102738 -8.101 5.46e-16 ***
## as.factor(DID)236 -1.105162 0.102937 -10.736 < 2e-16 ***
## as.factor(DID)237 -0.764733 0.105896 -7.222 5.14e-13 ***
## as.factor(DID)238 0.248673 0.116251 2.139 0.032427 *
## as.factor(DID)239 -0.895892 0.102392 -8.750 < 2e-16 ***
## as.factor(DID)240 -1.242176 0.102128 -12.163 < 2e-16 ***
## as.factor(DID)241 -2.369040 0.098180 -24.129 < 2e-16 ***
## as.factor(DID)242 -1.101073 0.103317 -10.657 < 2e-16 ***
## as.factor(DID)243 -1.057710 0.108492 -9.749 < 2e-16 ***
## as.factor(DID)244 -0.560656 0.110773 -5.061 4.16e-07 ***
## as.factor(DID)245 0.261619 0.125199 2.090 0.036652 *
## as.factor(DID)246 -1.706840 0.100013 -17.066 < 2e-16 ***
## as.factor(DID)247 -1.234545 0.107606 -11.473 < 2e-16 ***
## as.factor(DID)248 -0.945059 0.099728 -9.476 < 2e-16 ***
## as.factor(DID)249 0.026476 0.124731 0.212 0.831901
## as.factor(DID)250 -0.721227 0.105067 -6.864 6.68e-12 ***
## as.factor(DID)251 -0.688719 0.106677 -6.456 1.07e-10 ***
## as.factor(DID)252 -0.913442 0.103706 -8.808 < 2e-16 ***
## as.factor(DID)253 -0.511879 0.111901 -4.574 4.78e-06 ***
## as.factor(DID)254 -1.762981 0.115162 -15.309 < 2e-16 ***
## as.factor(DID)255 -0.856150 0.108863 -7.864 3.71e-15 ***
## as.factor(DID)256 -0.251852 0.106913 -2.356 0.018489 *
## as.factor(DID)257 0.505590 0.140392 3.601 0.000317 ***
## as.factor(DID)258 -1.562184 0.098968 -15.785 < 2e-16 ***
## as.factor(DID)259 -0.846290 0.107113 -7.901 2.77e-15 ***
## as.factor(DID)260 -0.132057 0.108378 -1.218 0.223037
## as.factor(DID)261 -2.301424 0.098132 -23.452 < 2e-16 ***
## as.factor(DID)262 -0.725316 0.103528 -7.006 2.45e-12 ***
## as.factor(DID)263 -0.414257 0.107373 -3.858 0.000114 ***
## as.factor(DID)264 -0.444065 0.105615 -4.205 2.62e-05 ***
## as.factor(DID)265 -0.075139 0.114318 -0.657 0.510998
## as.factor(DID)266 0.615342 0.130073 4.731 2.24e-06 ***
## as.factor(DID)267 -0.257368 0.106773 -2.410 0.015934 *
## as.factor(DID)268 -1.115655 0.104103 -10.717 < 2e-16 ***
## as.factor(DID)269 -0.143548 0.111944 -1.282 0.199729
## as.factor(DID)270 -0.052250 0.111599 -0.468 0.639648
## as.factor(DID)271 0.056815 0.115452 0.492 0.622643
## as.factor(DID)272 -0.088445 0.112363 -0.787 0.431202
## as.factor(DID)273 -0.338363 0.109176 -3.099 0.001940 **
## as.factor(DID)274 -0.169419 0.120699 -1.404 0.160423
## as.factor(DID)275 -1.110405 0.101896 -10.897 < 2e-16 ***
## as.factor(DID)276 -0.187979 0.106426 -1.766 0.077348 .
## as.factor(DID)277 -0.200188 0.146869 -1.363 0.172873
## as.factor(DID)278 -0.791947 0.098415 -8.047 8.49e-16 ***
## as.factor(DID)279 -1.831521 0.099957 -18.323 < 2e-16 ***
## as.factor(DID)280 -1.045823 0.102210 -10.232 < 2e-16 ***
## as.factor(DID)281 -0.817280 0.101859 -8.024 1.03e-15 ***
## as.factor(DID)282 -0.777911 0.109042 -7.134 9.75e-13 ***
## as.factor(DID)284 -0.782806 0.104522 -7.489 6.92e-14 ***
## as.factor(DID)287 -1.035915 0.104583 -9.905 < 2e-16 ***
## as.factor(DID)289 -0.883532 0.101090 -8.740 < 2e-16 ***
## as.factor(DID)290 -1.049015 0.104241 -10.063 < 2e-16 ***
## as.factor(DID)315 -0.730629 0.115409 -6.331 2.44e-10 ***
## as.factor(DID)316 -1.090704 0.103373 -10.551 < 2e-16 ***
## as.factor(DID)318 -1.666857 0.105254 -15.837 < 2e-16 ***
## as.factor(DID)319 -2.273787 0.098779 -23.019 < 2e-16 ***
## as.factor(DID)320 -1.525834 0.104652 -14.580 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 269656 on 245600 degrees of freedom
## AIC: 269950
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 415.55, df = 8, p-value < 2.2e-16exponential transformation
exp(glm_child$coefficients)## (Intercept) n_children_in_household PR004_PR009_01
## 8.68426215 0.97189458 1.41491756
## C002_01 as.factor(DID)147 as.factor(DID)148
## 0.51468855 0.54465757 0.91028798
## as.factor(DID)149 as.factor(DID)150 as.factor(DID)151
## 0.44727950 0.45656288 0.82631471
## as.factor(DID)152 as.factor(DID)153 as.factor(DID)154
## 0.71571648 0.40624870 0.50750152
## as.factor(DID)155 as.factor(DID)156 as.factor(DID)157
## 0.43067852 0.26888624 0.63627844
## as.factor(DID)158 as.factor(DID)159 as.factor(DID)160
## 0.70899315 0.26994651 0.47789348
## as.factor(DID)161 as.factor(DID)162 as.factor(DID)163
## 0.27869861 1.15104847 1.07902058
## as.factor(DID)164 as.factor(DID)165 as.factor(DID)166
## 0.56755636 0.37570439 0.43566418
## as.factor(DID)167 as.factor(DID)169 as.factor(DID)170
## 1.66479663 0.17840212 0.62322241
## as.factor(DID)171 as.factor(DID)172 as.factor(DID)173
## 0.64561636 0.52807089 0.76555125
## as.factor(DID)174 as.factor(DID)175 as.factor(DID)176
## 0.38415219 0.26401140 0.62350291
## as.factor(DID)177 as.factor(DID)178 as.factor(DID)179
## 0.42650577 1.22386457 0.48762652
## as.factor(DID)180 as.factor(DID)181 as.factor(DID)182
## 0.50733088 0.88907702 0.24936704
## as.factor(DID)183 as.factor(DID)184 as.factor(DID)185
## 0.51703930 0.27053703 1.33583600
## as.factor(DID)186 as.factor(DID)187 as.factor(DID)188
## 0.18382953 0.23287662 0.27206914
## as.factor(DID)189 as.factor(DID)190 as.factor(DID)191
## 0.69913479 0.37003145 0.40438937
## as.factor(DID)192 as.factor(DID)193 as.factor(DID)194
## 0.34656849 0.94109585 0.18563282
## as.factor(DID)195 as.factor(DID)196 as.factor(DID)197
## 0.15217520 0.49712841 0.23947388
## as.factor(DID)198 as.factor(DID)199 as.factor(DID)200
## 0.16775016 1.42165604 0.23287626
## as.factor(DID)202 as.factor(DID)203 as.factor(DID)204
## 0.22845019 0.23631521 0.40863100
## as.factor(DID)205 as.factor(DID)206 as.factor(DID)207
## 0.20450233 0.19163614 0.13145413
## as.factor(DID)208 as.factor(DID)209 as.factor(DID)210
## 0.22004826 0.35783680 0.17847180
## as.factor(DID)211 as.factor(DID)212 as.factor(DID)213
## 0.35702078 0.26061459 0.20551759
## as.factor(DID)214 as.factor(DID)215 as.factor(DID)216
## 0.12300538 0.25666877 0.18714385
## as.factor(DID)217 as.factor(DID)218 as.factor(DID)219
## 0.18088477 0.19509408 0.21377515
## as.factor(DID)220 as.factor(DID)221 as.factor(DID)222
## 0.12208693 0.08580172 0.18406963
## as.factor(DID)223 as.factor(DID)224 as.factor(DID)225
## 0.20107914 0.11671582 0.15679900
## as.factor(DID)226 as.factor(DID)227 as.factor(DID)228
## 0.24954195 0.18682063 0.10466013
## as.factor(DID)229 as.factor(DID)230 as.factor(DID)231
## 0.13925361 0.11794719 0.24562354
## as.factor(DID)232 as.factor(DID)233 as.factor(DID)234
## 0.74601182 0.10325112 0.20515743
## as.factor(DID)235 as.factor(DID)236 as.factor(DID)237
## 0.43506233 0.33115733 0.46545817
## as.factor(DID)238 as.factor(DID)239 as.factor(DID)240
## 1.28232283 0.40824317 0.28875517
## as.factor(DID)241 as.factor(DID)242 as.factor(DID)243
## 0.09357048 0.33251418 0.34725018
## as.factor(DID)244 as.factor(DID)245 as.factor(DID)246
## 0.57083429 1.29903108 0.18143825
## as.factor(DID)247 as.factor(DID)248 as.factor(DID)249
## 0.29096723 0.38865667 1.02682949
## as.factor(DID)250 as.factor(DID)251 as.factor(DID)252
## 0.48615558 0.50221916 0.40114117
## as.factor(DID)253 as.factor(DID)254 as.factor(DID)255
## 0.59936809 0.17153272 0.42479420
## as.factor(DID)256 as.factor(DID)257 as.factor(DID)258
## 0.77736012 1.65796325 0.20967757
## as.factor(DID)259 as.factor(DID)260 as.factor(DID)261
## 0.42900378 0.87629061 0.10011614
## as.factor(DID)262 as.factor(DID)263 as.factor(DID)264
## 0.48417163 0.66083096 0.64142358
## as.factor(DID)265 as.factor(DID)266 as.factor(DID)267
## 0.92761446 1.85028857 0.77308363
## as.factor(DID)268 as.factor(DID)269 as.factor(DID)270
## 0.32770047 0.86627896 0.94909197
## as.factor(DID)271 as.factor(DID)272 as.factor(DID)273
## 1.05845992 0.91535372 0.71293627
## as.factor(DID)274 as.factor(DID)275 as.factor(DID)276
## 0.84415517 0.32942564 0.82863226
## as.factor(DID)277 as.factor(DID)278 as.factor(DID)279
## 0.81857708 0.45296190 0.16016976
## as.factor(DID)280 as.factor(DID)281 as.factor(DID)282
## 0.35140253 0.44163131 0.45936450
## as.factor(DID)284 as.factor(DID)287 as.factor(DID)289
## 0.45712131 0.35490148 0.41332046
## as.factor(DID)290 as.factor(DID)315 as.factor(DID)316
## 0.35028265 0.48160583 0.33598000
## as.factor(DID)318 as.factor(DID)319 as.factor(DID)320
## 0.18883967 0.10292172 0.21743960confidence interval (intercept and coefficient)
# confint(glm_child, level = 0.95)exponential transformation of confidence interval (odds ratio of intercept and coefficient)
# exp(confint(glm_child, level = 0.95))AIC
extractAIC(glm_child)## [1] 147.0 269950.4BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 147 271481effectiveness of explanatory variables
glm_child_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy)
anova(glm_child_null, glm_child, test = "Chisq")variables selection
# step(glm_child_null, direction = "both",
# scope = (~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)))multicolinearity
vif(glm_child)## GVIF Df GVIF^(1/(2*Df))
## n_children_in_household 1.146849 1 1.070910
## PR004_PR009_01 1.246852 1 1.116625
## C002_01 1.024292 1 1.012073
## as.factor(DID) 1.441880 143 1.001280Districts with P-Values Bigger Than 0.05 and Coefficient
glm_summary <- glm_child %>% summary()
glm_coef <- as.data.frame(glm_summary$coefficients)
glm_coef %>% filter(`Pr(>|z|)` > 0.05)Districts with P-Values Bigger Than 0.05
glm_coef %>% filter(`Pr(>|z|)` > 0.05) %>% t() %>% .[-c(1:5),]## as.factor(DID)148 as.factor(DID)151 as.factor(DID)162 as.factor(DID)163
## as.factor(DID)178 as.factor(DID)181 as.factor(DID)193 as.factor(DID)249
## as.factor(DID)260 as.factor(DID)265 as.factor(DID)269 as.factor(DID)270
## as.factor(DID)271 as.factor(DID)272 as.factor(DID)274 as.factor(DID)276
## as.factor(DID)277dists_not_fit <- c(148, 151, 162, 163, 178, 181, 193, 249, 260, 265, 269, 270, 271, 272, 274, 276, 277)
child_ica_dummy %>% filter(DID == dists_not_fit) %>% select(DID, DNAME) %>% summarize(DID = unique(DID), DNAME = unique(DNAME))## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていませんDistricts with P-Values Bigger Than 0.001 and Coefficient
glm_summary <- glm_child %>% summary()
glm_coef <- as.data.frame(glm_summary$coefficients)
glm_coef %>% filter(`Pr(>|z|)` > 0.001)Districts with P-Values Bigger Than 0.001
glm_coef %>% filter(`Pr(>|z|)` > 0.001) %>% t() %>% .[-c(1:5),]## as.factor(DID)148 as.factor(DID)151 as.factor(DID)152 as.factor(DID)158
## as.factor(DID)162 as.factor(DID)163 as.factor(DID)173 as.factor(DID)178
## as.factor(DID)181 as.factor(DID)185 as.factor(DID)193 as.factor(DID)199
## as.factor(DID)232 as.factor(DID)238 as.factor(DID)245 as.factor(DID)249
## as.factor(DID)256 as.factor(DID)260 as.factor(DID)265 as.factor(DID)267
## as.factor(DID)269 as.factor(DID)270 as.factor(DID)271 as.factor(DID)272
## as.factor(DID)273 as.factor(DID)274 as.factor(DID)276 as.factor(DID)277dists_not_fit <- c(148, 151, 152, 158, 162, 163, 173, 178, 181, 185, 193, 199, 232, 238, 245, 249, 256, 260, 265, 267, 269, 270, 271, 272, 273, 274, 276, 277)
child_ica_dummy %>% filter(DID == dists_not_fit) %>% select(DID, DNAME) %>% summarize(DID = unique(DID), DNAME = unique(DNAME))## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていませんDists_not_fit. GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01
All of dists_not_fit
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(VID), family = "binomial", data = child_ica_dummy %>% filter(DID %in% dists_not_fit))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + as.factor(VID), family = "binomial", data = child_ica_dummy %>%
## filter(DID %in% dists_not_fit))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9975 0.2123 0.4372 0.5768 1.4738
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.702857 0.485396 3.508 0.000451 ***
## n_children_in_household 0.036001 0.012295 2.928 0.003410 **
## PR004_PR009_01 0.085176 0.040443 2.106 0.035197 *
## C002_01 -0.181653 0.028957 -6.273 3.54e-10 ***
## as.factor(VID)4937 -0.475516 0.669974 -0.710 0.477857
## as.factor(VID)4938 1.793771 1.123676 1.596 0.110412
## as.factor(VID)4939 1.106393 0.872792 1.268 0.204924
## as.factor(VID)4940 0.394658 0.715379 0.552 0.581170
## as.factor(VID)4941 -0.345331 0.662635 -0.521 0.602264
## as.factor(VID)4942 1.897602 1.122701 1.690 0.090987 .
## as.factor(VID)4943 -1.210639 0.566444 -2.137 0.032577 *
## as.factor(VID)4944 0.575980 0.774371 0.744 0.456995
## as.factor(VID)4945 0.078486 0.614769 0.128 0.898411
## as.factor(VID)4946 0.527867 0.712682 0.741 0.458890
## as.factor(VID)4947 -0.398360 0.623935 -0.638 0.523172
## as.factor(VID)4948 1.326896 0.869342 1.526 0.126929
## as.factor(VID)4949 -0.512888 0.613484 -0.836 0.403141
## as.factor(VID)4950 2.151462 1.119483 1.922 0.054627 .
## as.factor(VID)4951 0.858204 0.768481 1.117 0.264100
## as.factor(VID)4952 -0.381300 0.580474 -0.657 0.511260
## as.factor(VID)4953 1.603477 0.866352 1.851 0.064193 .
## as.factor(VID)4954 -1.093095 0.577997 -1.891 0.058601 .
## as.factor(VID)4955 -0.050238 0.633927 -0.079 0.936835
## as.factor(VID)4956 -1.544766 0.538628 -2.868 0.004131 **
## as.factor(VID)4957 0.048790 0.682060 0.072 0.942973
## as.factor(VID)4958 -0.811464 0.565449 -1.435 0.151264
## as.factor(VID)4959 15.776122 688.099301 0.023 0.981708
## as.factor(VID)4966 -0.047350 0.616762 -0.077 0.938805
## as.factor(VID)4991 15.732527 482.918751 0.033 0.974011
## as.factor(VID)4992 0.927659 0.767274 1.209 0.226650
## as.factor(VID)4993 0.508508 0.775083 0.656 0.511780
## as.factor(VID)4994 2.147531 1.119164 1.919 0.055001 .
## as.factor(VID)4995 0.475163 0.713868 0.666 0.505656
## as.factor(VID)4996 0.240871 0.678371 0.355 0.722535
## as.factor(VID)4997 1.789771 1.124045 1.592 0.111326
## as.factor(VID)4998 0.468174 0.775809 0.603 0.546200
## as.factor(VID)4999 1.570511 0.866595 1.812 0.069943 .
## as.factor(VID)5000 0.492290 0.712538 0.691 0.489631
## as.factor(VID)5001 0.661238 0.771970 0.857 0.391689
## as.factor(VID)5002 -0.341784 0.641394 -0.533 0.594119
## as.factor(VID)14940 0.561585 0.712159 0.789 0.430365
## as.factor(VID)14941 0.316169 0.647988 0.488 0.625604
## as.factor(VID)14942 -0.630604 0.604462 -1.043 0.296833
## as.factor(VID)14943 0.467471 0.674064 0.694 0.487989
## as.factor(VID)14944 0.461904 0.674483 0.685 0.493453
## as.factor(VID)14945 2.049224 1.120379 1.829 0.067393 .
## as.factor(VID)14947 2.057430 1.120162 1.837 0.066250 .
## as.factor(VID)14950 0.296842 0.648604 0.458 0.647194
## as.factor(VID)14953 -0.062520 0.634450 -0.099 0.921501
## as.factor(VID)14954 -0.374583 0.588340 -0.637 0.524335
## as.factor(VID)14955 0.327471 0.676200 0.484 0.628186
## as.factor(VID)14960 1.302113 0.869561 1.497 0.134279
## as.factor(VID)14961 1.189938 0.871560 1.365 0.172160
## as.factor(VID)14963 0.255933 0.677879 0.378 0.705765
## as.factor(VID)14968 0.908239 0.767804 1.183 0.236847
## as.factor(VID)14974 15.765453 602.859981 0.026 0.979137
## as.factor(VID)14989 -1.924316 0.557627 -3.451 0.000559 ***
## as.factor(VID)14992 0.626077 0.712945 0.878 0.379859
## as.factor(VID)15044 0.031497 0.615695 0.051 0.959201
## as.factor(VID)15045 0.507392 0.713008 0.712 0.476699
## as.factor(VID)15046 -0.193405 0.605654 -0.319 0.749475
## as.factor(VID)15047 1.124288 0.764798 1.470 0.141549
## as.factor(VID)15048 15.738471 475.904668 0.033 0.973618
## as.factor(VID)15049 0.239993 0.649539 0.369 0.711768
## as.factor(VID)15050 1.059699 0.765894 1.384 0.166478
## as.factor(VID)15051 0.087376 0.614193 0.142 0.886873
## as.factor(VID)15052 2.162975 1.119121 1.933 0.053268 .
## as.factor(VID)15053 1.549129 0.866415 1.788 0.073780 .
## as.factor(VID)15054 2.442462 1.117262 2.186 0.028807 *
## as.factor(VID)15055 15.760623 465.867725 0.034 0.973012
## as.factor(VID)15056 0.016514 0.653380 0.025 0.979836
## as.factor(VID)15057 -0.311420 0.608224 -0.512 0.608641
## as.factor(VID)15058 0.560069 0.672762 0.832 0.405131
## as.factor(VID)15059 -0.745893 0.581300 -1.283 0.199441
## as.factor(VID)15060 -0.317363 0.598154 -0.531 0.595716
## as.factor(VID)15061 0.087698 0.601177 0.146 0.884018
## as.factor(VID)15062 0.168002 0.629668 0.267 0.789615
## as.factor(VID)15063 -0.595903 0.577550 -1.032 0.302176
## as.factor(VID)15064 0.343953 0.626848 0.549 0.583210
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## as.factor(VID)46975 1.605362 0.866887 1.852 0.064045 .
## as.factor(VID)46976 0.121130 0.590994 0.205 0.837603
## as.factor(VID)46981 0.628149 0.644642 0.974 0.329851
## as.factor(VID)46984 0.467598 0.626099 0.747 0.455158
## as.factor(VID)46986 -0.195204 0.585239 -0.334 0.738722
## as.factor(VID)46988 0.240343 0.612833 0.392 0.694923
## as.factor(VID)46991 0.202065 0.589864 0.343 0.731928
## as.factor(VID)47025 2.232469 1.118523 1.996 0.045944 *
## as.factor(VID)47041 -0.704012 0.548189 -1.284 0.199054
## as.factor(VID)47043 -0.413714 0.626311 -0.661 0.508897
## as.factor(VID)47108 0.276550 0.628373 0.440 0.659861
## as.factor(VID)47120 1.990463 1.121564 1.775 0.075944 .
## as.factor(VID)47145 -0.244705 0.561061 -0.436 0.662731
## as.factor(VID)47146 -0.456663 0.551549 -0.828 0.407691
## as.factor(VID)47150 -0.598387 0.552671 -1.083 0.278934
## as.factor(VID)47153 -0.390709 0.558023 -0.700 0.483824
## as.factor(VID)47154 0.186194 0.600152 0.310 0.756375
## as.factor(VID)47156 0.602640 0.644610 0.935 0.349844
## as.factor(VID)47157 -0.370700 0.557864 -0.664 0.506370
## as.factor(VID)47158 0.099284 0.582261 0.171 0.864606
## as.factor(VID)47160 0.192511 0.599937 0.321 0.748297
## as.factor(VID)47291 -0.153570 0.593777 -0.259 0.795918
## as.factor(VID)47295 -1.102737 0.567712 -1.942 0.052086 .
## as.factor(VID)47296 2.487725 1.116943 2.227 0.025930 *
## as.factor(VID)47297 -0.583901 0.577203 -1.012 0.311728
## as.factor(VID)47298 0.386300 0.676141 0.571 0.567776
## as.factor(VID)47299 -0.085749 0.576048 -0.149 0.881666
## as.factor(VID)47300 -0.964665 0.587783 -1.641 0.100757
## as.factor(VID)47301 0.088483 0.601117 0.147 0.882975
## as.factor(VID)47302 2.257053 1.118955 2.017 0.043684 *
## as.factor(VID)47303 -1.764014 0.523868 -3.367 0.000759 ***
## as.factor(VID)47304 15.789555 518.895611 0.030 0.975725
## as.factor(VID)47305 0.283663 0.648692 0.437 0.661905
## as.factor(VID)47306 -0.163797 0.595391 -0.275 0.783233
## as.factor(VID)47308 0.778488 0.669757 1.162 0.245096
## as.factor(VID)47312 -0.805214 0.560241 -1.437 0.150643
## as.factor(VID)47313 0.955393 0.706826 1.352 0.176482
## as.factor(VID)47318 -0.345227 0.562451 -0.614 0.539354
## as.factor(VID)47327 2.562371 1.116831 2.294 0.021772 *
## as.factor(VID)47332 0.392205 0.611203 0.642 0.521073
## as.factor(VID)47338 0.070826 0.601863 0.118 0.906323
## as.factor(VID)47339 -1.521528 0.538599 -2.825 0.004728 **
## as.factor(VID)47345 15.805475 519.074198 0.030 0.975709
## as.factor(VID)47352 0.149910 0.581358 0.258 0.796514
## as.factor(VID)47355 15.722019 486.682243 0.032 0.974229
## as.factor(VID)47356 0.492063 0.646052 0.762 0.446271
## as.factor(VID)47409 -0.531779 0.570153 -0.933 0.350977
## as.factor(VID)47582 -0.658947 0.551248 -1.195 0.231941
## as.factor(VID)57619 -0.072776 0.604272 -0.120 0.904138
## as.factor(VID)57636 15.796454 523.532430 0.030 0.975929
## as.factor(VID)57641 2.176013 1.119156 1.944 0.051855 .
## as.factor(VID)57643 2.295024 1.118420 2.052 0.040167 *
## as.factor(VID)57644 0.568646 0.672774 0.845 0.397985
## as.factor(VID)57646 -0.295638 0.597261 -0.495 0.620607
## as.factor(VID)57648 0.837509 0.669554 1.251 0.210991
## as.factor(VID)67891 0.031481 0.632009 0.050 0.960273
## as.factor(VID)67936 15.775771 641.018933 0.025 0.980366
## as.factor(VID)67944 -0.125314 0.618708 -0.203 0.839494
## as.factor(VID)67952 1.560857 1.126862 1.385 0.166011
## as.factor(VID)67958 15.781843 582.770380 0.027 0.978395
## as.factor(VID)67961 15.821342 678.043819 0.023 0.981384
## as.factor(VID)67963 -0.318569 0.597643 -0.533 0.594004
## as.factor(VID)67965 0.977977 0.766847 1.275 0.202195
## as.factor(VID)68080 1.127498 0.764962 1.474 0.140501
## as.factor(VID)68083 1.081570 0.765144 1.414 0.157493
## as.factor(VID)78079 1.374272 0.762279 1.803 0.071412 .
## as.factor(VID)78205 15.784982 589.157368 0.027 0.978625
## as.factor(VID)78214 0.026923 0.632497 0.043 0.966048
## as.factor(VID)78215 1.550169 0.866733 1.789 0.073692 .
## as.factor(VID)78231 2.288708 1.118076 2.047 0.040657 *
## as.factor(VID)78232 -0.372018 0.573657 -0.649 0.516659
## as.factor(VID)78233 15.739795 498.063149 0.032 0.974789
## as.factor(VID)78234 0.301659 0.629624 0.479 0.631861
## as.factor(VID)78235 15.744419 506.152991 0.031 0.975185
## as.factor(VID)78236 -0.709223 0.563773 -1.258 0.208394
## as.factor(VID)78237 0.004801 0.591431 0.008 0.993523
## as.factor(VID)78238 15.752656 514.709173 0.031 0.975585
## as.factor(VID)78239 -1.988252 0.550100 -3.614 0.000301 ***
## as.factor(VID)78240 -1.310650 0.556127 -2.357 0.018436 *
## as.factor(VID)78241 15.743716 510.359310 0.031 0.975391
## as.factor(VID)78242 0.872311 0.709179 1.230 0.218686
## as.factor(VID)78243 -1.283590 0.530650 -2.419 0.015567 *
## as.factor(VID)78244 -1.246037 0.536366 -2.323 0.020173 *
## as.factor(VID)78245 15.743881 523.589969 0.030 0.976012
## as.factor(VID)78246 -1.106187 0.549741 -2.012 0.044199 *
## as.factor(VID)78247 15.708400 416.447914 0.038 0.969911
## as.factor(VID)78248 -0.033518 0.602772 -0.056 0.955655
## as.factor(VID)78249 -0.995070 0.547815 -1.816 0.069304 .
## as.factor(VID)78250 15.785718 486.524569 0.032 0.974116
## as.factor(VID)78251 -1.576847 0.561215 -2.810 0.004959 **
## as.factor(VID)78252 -0.695917 0.581068 -1.198 0.231052
## as.factor(VID)78253 2.262027 1.118899 2.022 0.043212 *
## as.factor(VID)78254 1.751807 0.865985 2.023 0.043083 *
## as.factor(VID)78255 15.830001 589.309921 0.027 0.978570
## as.factor(VID)78256 15.863330 564.771063 0.028 0.977592
## as.factor(VID)78257 15.791484 459.523260 0.034 0.972586
## as.factor(VID)78258 -2.267406 0.547946 -4.138 3.50e-05 ***
## as.factor(VID)78259 -1.129091 0.563852 -2.002 0.045235 *
## as.factor(VID)78260 -0.243663 0.596842 -0.408 0.683088
## as.factor(VID)78261 -0.293771 0.623220 -0.471 0.637373
## as.factor(VID)78262 15.818045 688.251716 0.023 0.981664
## as.factor(VID)78263 2.338700 1.117720 2.092 0.036404 *
## as.factor(VID)78266 0.087066 0.602409 0.145 0.885083
## as.factor(VID)78267 0.842959 0.769826 1.095 0.273517
## as.factor(VID)78268 -1.412754 0.545757 -2.589 0.009636 **
## as.factor(VID)78269 -1.541264 0.542124 -2.843 0.004469 **
## as.factor(VID)78270 2.481996 1.117535 2.221 0.026354 *
## as.factor(VID)78271 15.821523 486.556290 0.033 0.974059
## as.factor(VID)78272 2.299845 1.119029 2.055 0.039858 *
## as.factor(VID)78273 1.385150 0.762101 1.818 0.069134 .
## as.factor(VID)78276 1.780017 0.865352 2.057 0.039688 *
## as.factor(VID)78277 -0.338993 0.598968 -0.566 0.571420
## as.factor(VID)78279 0.273566 0.599968 0.456 0.648413
## as.factor(VID)78283 0.214436 0.589283 0.364 0.715938
## as.factor(VID)78285 15.803299 490.303674 0.032 0.974287
## as.factor(VID)78286 15.791458 447.561366 0.035 0.971854
## as.factor(VID)78288 2.346093 1.118685 2.097 0.035977 *
## as.factor(VID)78291 0.140548 0.601302 0.234 0.815188
## as.factor(VID)78292 15.795047 625.050119 0.025 0.979840
## as.factor(VID)78295 2.389088 1.118482 2.136 0.032679 *
## as.factor(VID)78298 15.761151 510.187737 0.031 0.975355
## as.factor(VID)78300 1.620474 0.866619 1.870 0.061500 .
## as.factor(VID)78301 1.554667 0.866750 1.794 0.072865 .
## as.factor(VID)78302 -0.927305 0.558781 -1.660 0.097012 .
## as.factor(VID)78303 2.248605 1.119261 2.009 0.044536 *
## as.factor(VID)78309 0.186767 0.722130 0.259 0.795918
## as.factor(VID)78312 0.642393 0.671884 0.956 0.339018
## as.factor(VID)78313 15.753804 514.346726 0.031 0.975566
## as.factor(VID)78542 -0.711497 0.575134 -1.237 0.216051
## as.factor(VID)78592 0.892926 0.707791 1.262 0.207105
## as.factor(VID)78593 0.258996 0.599295 0.432 0.665619
## as.factor(VID)78594 1.655915 0.866624 1.911 0.056035 .
## as.factor(VID)78595 0.600128 0.645188 0.930 0.352289
## as.factor(VID)78596 -0.057576 0.584194 -0.099 0.921491
## as.factor(VID)78597 0.272549 0.628454 0.434 0.664520
## as.factor(VID)78598 1.237141 0.764049 1.619 0.105406
## as.factor(VID)78599 0.992409 0.707280 1.403 0.160576
## as.factor(VID)78600 -0.119587 0.659266 -0.181 0.856058
## as.factor(VID)78601 0.618837 0.624635 0.991 0.321823
## as.factor(VID)78602 -0.117786 0.619920 -0.190 0.849307
## as.factor(VID)78604 -0.026189 0.633961 -0.041 0.967049
## as.factor(VID)78605 -1.527879 0.537106 -2.845 0.004446 **
## as.factor(VID)78606 0.260018 0.599983 0.433 0.664743
## as.factor(VID)78607 1.617264 0.866599 1.866 0.062011 .
## as.factor(VID)78608 -1.831664 0.578401 -3.167 0.001541 **
## as.factor(VID)78609 0.037324 0.582709 0.064 0.948929
## as.factor(VID)78611 -0.132736 0.570417 -0.233 0.815994
## as.factor(VID)78613 0.032167 0.603057 0.053 0.957461
## as.factor(VID)78614 1.646500 1.126585 1.461 0.143879
## as.factor(VID)78616 -1.151708 0.588214 -1.958 0.050233 .
## as.factor(VID)78617 0.808014 0.710055 1.138 0.255137
## as.factor(VID)78619 0.939569 0.767507 1.224 0.220883
## as.factor(VID)78638 0.743653 0.710549 1.047 0.295289
## as.factor(VID)78643 0.084692 0.581771 0.146 0.884256
## as.factor(VID)78649 -1.113646 0.546270 -2.039 0.041486 *
## as.factor(VID)78650 1.422021 0.868324 1.638 0.101492
## as.factor(VID)78651 1.302766 0.703869 1.851 0.064189 .
## as.factor(VID)78652 -0.598458 0.566787 -1.056 0.291024
## as.factor(VID)78654 -0.366908 0.609070 -0.602 0.546904
## as.factor(VID)78683 2.355678 1.117978 2.107 0.035110 *
## as.factor(VID)78687 0.833156 0.708954 1.175 0.239919
## as.factor(VID)78689 0.168448 0.613980 0.274 0.783812
## as.factor(VID)78691 0.708219 0.671415 1.055 0.291509
## as.factor(VID)78693 0.580813 0.646215 0.899 0.368764
## as.factor(VID)78694 -0.107306 0.592706 -0.181 0.856332
## as.factor(VID)78695 0.085388 0.614725 0.139 0.889526
## as.factor(VID)78696 0.225318 0.629479 0.358 0.720385
## as.factor(VID)78698 1.345215 0.762844 1.763 0.077829 .
## as.factor(VID)78699 0.021065 0.615911 0.034 0.972717
## as.factor(VID)78700 0.382819 0.676908 0.566 0.571706
## as.factor(VID)78701 -0.482984 0.583569 -0.828 0.407875
## as.factor(VID)78703 0.555820 0.645901 0.861 0.389494
## as.factor(VID)78704 1.114903 0.705257 1.581 0.113913
## as.factor(VID)78705 0.442459 0.625850 0.707 0.479584
## as.factor(VID)78706 0.363225 0.648378 0.560 0.575339
## as.factor(VID)78708 0.059886 0.632239 0.095 0.924536
## as.factor(VID)78709 0.166935 0.651516 0.256 0.797777
## as.factor(VID)78711 0.703494 0.670946 1.049 0.294403
## as.factor(VID)78712 0.108207 0.630624 0.172 0.863762
## as.factor(VID)78713 1.321680 0.869359 1.520 0.128437
## as.factor(VID)78717 1.671492 0.865589 1.931 0.053477 .
## as.factor(VID)78718 0.189608 0.629416 0.301 0.763227
## as.factor(VID)78739 0.275108 0.628039 0.438 0.661355
## as.factor(VID)78741 0.452720 0.647100 0.700 0.484169
## as.factor(VID)78742 -0.344545 0.588934 -0.585 0.558526
## as.factor(VID)78744 -0.066329 0.604724 -0.110 0.912660
## as.factor(VID)78745 0.187966 0.614178 0.306 0.759570
## as.factor(VID)78746 0.215480 0.629842 0.342 0.732263
## as.factor(VID)78748 0.116072 0.614800 0.189 0.850253
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 37106 on 47598 degrees of freedom
## Residual deviance: 32845 on 46789 degrees of freedom
## AIC: 34465
##
## Number of Fisher Scoring iterations: 16## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 6.8991, df = 8, p-value = 0.5476148
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 148))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 148))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1602 0.4780 0.5202 0.5651 0.6721
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.08964 0.22845 9.147 <2e-16 ***
## n_children_in_household -0.05985 0.04872 -1.228 0.219
## PR004_PR009_01 0.20142 0.15237 1.322 0.186
## C002_01 -0.29794 0.13730 -2.170 0.030 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1462.2 on 1851 degrees of freedom
## Residual deviance: 1454.1 on 1848 degrees of freedom
## AIC: 1462.1
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 0.70355, df = 8, p-value = 0.9995151
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 151))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 151))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0776 0.5003 0.5051 0.5338 0.7164
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.42858 0.25219 5.665 1.47e-08 ***
## n_children_in_household -0.01015 0.05416 -0.187 0.851379
## PR004_PR009_01 0.61702 0.16072 3.839 0.000123 ***
## C002_01 -0.12832 0.13800 -0.930 0.352425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1445.1 on 1793 degrees of freedom
## Residual deviance: 1430.1 on 1790 degrees of freedom
## AIC: 1438.1
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 26.905, df = 8, p-value = 0.0007342260
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 260))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 260))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1956 0.4753 0.5348 0.5847 0.7639
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.48695 0.21355 11.646 <2e-16 ***
## n_children_in_household -0.08535 0.03699 -2.308 0.0210 *
## PR004_PR009_01 -0.27317 0.14617 -1.869 0.0616 .
## C002_01 -0.27779 0.12815 -2.168 0.0302 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1650.2 on 2004 degrees of freedom
## Residual deviance: 1636.2 on 2001 degrees of freedom
## AIC: 1644.2
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 15.649, df = 8, p-value = 0.0477260, VID
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + factor(VID), family = "binomial", data = child_ica_dummy %>% filter(DID == 260))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + factor(VID), family = "binomial", data = child_ica_dummy %>%
## filter(DID == 260))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.98580 0.00014 0.42879 0.57612 1.18235
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.67422 0.48297 3.466 0.000527 ***
## n_children_in_household 0.03091 0.04526 0.683 0.494630
## PR004_PR009_01 0.06956 0.18637 0.373 0.708963
## C002_01 -0.12462 0.14000 -0.890 0.373392
## factor(VID)47295 -1.07596 0.53590 -2.008 0.044668 *
## factor(VID)47296 2.51948 1.10231 2.286 0.022276 *
## factor(VID)47297 -0.54884 0.54600 -1.005 0.314801
## factor(VID)47298 0.40838 0.64832 0.630 0.528759
## factor(VID)47299 -0.03990 0.55358 -0.072 0.942536
## factor(VID)47300 -0.94556 0.55578 -1.701 0.088881 .
## factor(VID)47301 0.12755 0.57501 0.222 0.824453
## factor(VID)47302 2.28506 1.10787 2.063 0.039154 *
## factor(VID)47303 -1.72351 0.50253 -3.430 0.000604 ***
## factor(VID)47304 16.81541 855.93786 0.020 0.984326
## factor(VID)47305 0.31982 0.62245 0.514 0.607388
## factor(VID)47306 -0.13887 0.57337 -0.242 0.808632
## factor(VID)47308 0.81913 0.64618 1.268 0.204919
## factor(VID)47312 -0.76830 0.53045 -1.448 0.147507
## factor(VID)47313 0.98803 0.68135 1.450 0.147027
## factor(VID)47318 -0.31342 0.53646 -0.584 0.559061
## factor(VID)47327 2.58615 1.10413 2.342 0.019168 *
## factor(VID)47332 0.42155 0.59249 0.711 0.476779
## factor(VID)47338 0.09982 0.57314 0.174 0.861735
## factor(VID)47339 -1.50129 0.51737 -2.902 0.003711 **
## factor(VID)47345 16.82728 856.13930 0.020 0.984319
## factor(VID)47352 0.19564 0.56564 0.346 0.729437
## factor(VID)47355 16.76490 802.63292 0.021 0.983335
## factor(VID)47356 0.52232 0.62125 0.841 0.400484
## factor(VID)47409 -0.49782 0.53881 -0.924 0.355524
## factor(VID)47582 -0.63324 0.52455 -1.207 0.227346
## factor(VID)57636 16.81954 863.54271 0.019 0.984460
## factor(VID)57643 2.31954 1.10185 2.105 0.035279 *
## factor(VID)57644 0.59762 0.64630 0.925 0.355130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1650.2 on 2004 degrees of freedom
## Residual deviance: 1365.7 on 1972 degrees of freedom
## AIC: 1431.7
##
## Number of Fisher Scoring iterations: 17## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 2.569, df = 8, p-value = 0.9584Without dists_not_fit. GLM C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID)
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy %>% filter(!DID %in% dists_not_fit))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + PR004_PR009_01 +
## C002_01 + as.factor(DID), family = "binomial", data = child_ica_dummy %>%
## filter(!DID %in% dists_not_fit))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5291 -1.1412 0.6592 0.8972 1.7474
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.20023 0.08886 24.761 < 2e-16 ***
## n_children_in_household -0.03221 0.00342 -9.417 < 2e-16 ***
## PR004_PR009_01 0.36964 0.01108 33.351 < 2e-16 ***
## C002_01 -0.73250 0.01017 -72.036 < 2e-16 ***
## as.factor(DID)147 -0.61110 0.10692 -5.715 1.09e-08 ***
## as.factor(DID)149 -0.80774 0.10286 -7.853 4.07e-15 ***
## as.factor(DID)150 -0.78424 0.10201 -7.688 1.50e-14 ***
## as.factor(DID)153 -0.90444 0.10406 -8.692 < 2e-16 ***
## as.factor(DID)154 -0.68180 0.10568 -6.452 1.11e-10 ***
## as.factor(DID)155 -0.84481 0.10119 -8.349 < 2e-16 ***
## as.factor(DID)156 -1.33487 0.11582 -11.525 < 2e-16 ***
## as.factor(DID)157 -0.45598 0.11098 -4.108 3.98e-05 ***
## as.factor(DID)159 -1.32982 0.10655 -12.481 < 2e-16 ***
## as.factor(DID)160 -0.73750 0.10489 -7.031 2.05e-12 ***
## as.factor(DID)161 -1.27916 0.09762 -13.103 < 2e-16 ***
## as.factor(DID)164 -0.57774 0.11384 -5.075 3.87e-07 ***
## as.factor(DID)165 -0.98013 0.10429 -9.398 < 2e-16 ***
## as.factor(DID)166 -0.83499 0.10473 -7.973 1.55e-15 ***
## as.factor(DID)167 0.50880 0.13040 3.902 9.55e-05 ***
## as.factor(DID)169 -1.72476 0.09928 -17.373 < 2e-16 ***
## as.factor(DID)170 -0.47695 0.10872 -4.387 1.15e-05 ***
## as.factor(DID)171 -0.44109 0.11072 -3.984 6.78e-05 ***
## as.factor(DID)172 -0.63932 0.10654 -6.001 1.96e-09 ***
## as.factor(DID)174 -0.96366 0.10561 -9.125 < 2e-16 ***
## as.factor(DID)175 -1.33897 0.10151 -13.191 < 2e-16 ***
## as.factor(DID)176 -0.48230 0.11479 -4.201 2.65e-05 ***
## as.factor(DID)177 -0.85401 0.10314 -8.280 < 2e-16 ***
## as.factor(DID)179 -0.72202 0.10558 -6.839 7.98e-12 ***
## as.factor(DID)180 -0.68083 0.10615 -6.414 1.42e-10 ***
## as.factor(DID)182 -1.39805 0.10043 -13.920 < 2e-16 ***
## as.factor(DID)183 -0.65956 0.10778 -6.120 9.39e-10 ***
## as.factor(DID)184 -1.30777 0.10005 -13.071 < 2e-16 ***
## as.factor(DID)186 -1.69771 0.09953 -17.058 < 2e-16 ***
## as.factor(DID)187 -1.46210 0.10117 -14.452 < 2e-16 ***
## as.factor(DID)188 -1.30548 0.10037 -13.006 < 2e-16 ***
## as.factor(DID)189 -0.35890 0.10834 -3.313 0.000924 ***
## as.factor(DID)190 -0.99623 0.10865 -9.169 < 2e-16 ***
## as.factor(DID)191 -0.90900 0.10845 -8.382 < 2e-16 ***
## as.factor(DID)192 -1.06589 0.10269 -10.380 < 2e-16 ***
## as.factor(DID)194 -1.68630 0.09992 -16.876 < 2e-16 ***
## as.factor(DID)195 -1.88739 0.10107 -18.674 < 2e-16 ***
## as.factor(DID)196 -0.70672 0.10997 -6.426 1.31e-10 ***
## as.factor(DID)197 -1.43308 0.10084 -14.211 < 2e-16 ***
## as.factor(DID)198 -1.79500 0.09975 -17.996 < 2e-16 ***
## as.factor(DID)200 -1.46069 0.10294 -14.190 < 2e-16 ***
## as.factor(DID)202 -1.48817 0.10096 -14.740 < 2e-16 ***
## as.factor(DID)203 -1.44779 0.09921 -14.593 < 2e-16 ***
## as.factor(DID)204 -0.89171 0.10343 -8.621 < 2e-16 ***
## as.factor(DID)205 -1.59378 0.09841 -16.196 < 2e-16 ***
## as.factor(DID)206 -1.65528 0.09837 -16.827 < 2e-16 ***
## as.factor(DID)207 -2.02987 0.09995 -20.309 < 2e-16 ***
## as.factor(DID)208 -1.51565 0.09950 -15.233 < 2e-16 ***
## as.factor(DID)209 -1.03418 0.10817 -9.560 < 2e-16 ***
## as.factor(DID)210 -1.72023 0.09790 -17.572 < 2e-16 ***
## as.factor(DID)211 -1.03761 0.10795 -9.611 < 2e-16 ***
## as.factor(DID)212 -1.34488 0.10228 -13.149 < 2e-16 ***
## as.factor(DID)213 -1.58217 0.09868 -16.033 < 2e-16 ***
## as.factor(DID)214 -2.10360 0.09920 -21.205 < 2e-16 ***
## as.factor(DID)215 -1.35788 0.09697 -14.003 < 2e-16 ***
## as.factor(DID)216 -1.68497 0.10160 -16.585 < 2e-16 ***
## as.factor(DID)217 -1.71022 0.09997 -17.107 < 2e-16 ***
## as.factor(DID)218 -1.64404 0.09735 -16.889 < 2e-16 ***
## as.factor(DID)219 -1.54732 0.10069 -15.366 < 2e-16 ***
## as.factor(DID)220 -2.09984 0.09644 -21.774 < 2e-16 ***
## as.factor(DID)221 -2.45952 0.10142 -24.251 < 2e-16 ***
## as.factor(DID)222 -1.69695 0.09785 -17.341 < 2e-16 ***
## as.factor(DID)223 -1.61343 0.10554 -15.288 < 2e-16 ***
## as.factor(DID)224 -2.15683 0.09912 -21.759 < 2e-16 ***
## as.factor(DID)225 -1.86448 0.09974 -18.693 < 2e-16 ***
## as.factor(DID)226 -1.38124 0.10295 -13.417 < 2e-16 ***
## as.factor(DID)227 -1.67171 0.09806 -17.047 < 2e-16 ***
## as.factor(DID)228 -2.25942 0.10320 -21.893 < 2e-16 ***
## as.factor(DID)229 -1.96803 0.10194 -19.306 < 2e-16 ***
## as.factor(DID)230 -2.14701 0.09950 -21.579 < 2e-16 ***
## as.factor(DID)231 -1.40315 0.09889 -14.188 < 2e-16 ***
## as.factor(DID)233 -2.27841 0.10437 -21.830 < 2e-16 ***
## as.factor(DID)234 -1.58568 0.10526 -15.065 < 2e-16 ***
## as.factor(DID)235 -0.83483 0.10293 -8.111 5.02e-16 ***
## as.factor(DID)236 -1.10526 0.10314 -10.717 < 2e-16 ***
## as.factor(DID)237 -0.76287 0.10610 -7.190 6.46e-13 ***
## as.factor(DID)239 -0.89327 0.10257 -8.708 < 2e-16 ***
## as.factor(DID)240 -1.24717 0.10232 -12.189 < 2e-16 ***
## as.factor(DID)241 -2.37464 0.09837 -24.140 < 2e-16 ***
## as.factor(DID)242 -1.09923 0.10351 -10.619 < 2e-16 ***
## as.factor(DID)243 -1.07306 0.10870 -9.872 < 2e-16 ***
## as.factor(DID)244 -0.56597 0.11096 -5.101 3.38e-07 ***
## as.factor(DID)246 -1.71773 0.10021 -17.142 < 2e-16 ***
## as.factor(DID)247 -1.23790 0.10783 -11.480 < 2e-16 ***
## as.factor(DID)248 -0.94198 0.09993 -9.427 < 2e-16 ***
## as.factor(DID)250 -0.72232 0.10526 -6.862 6.77e-12 ***
## as.factor(DID)251 -0.68896 0.10687 -6.447 1.14e-10 ***
## as.factor(DID)252 -0.91843 0.10390 -8.840 < 2e-16 ***
## as.factor(DID)253 -0.51896 0.11209 -4.630 3.66e-06 ***
## as.factor(DID)254 -1.76683 0.11543 -15.306 < 2e-16 ***
## as.factor(DID)255 -0.86631 0.10906 -7.943 1.97e-15 ***
## as.factor(DID)257 0.49870 0.14055 3.548 0.000388 ***
## as.factor(DID)258 -1.57776 0.09915 -15.912 < 2e-16 ***
## as.factor(DID)259 -0.84587 0.10732 -7.882 3.23e-15 ***
## as.factor(DID)261 -2.30250 0.09833 -23.416 < 2e-16 ***
## as.factor(DID)262 -0.72491 0.10371 -6.990 2.75e-12 ***
## as.factor(DID)263 -0.41112 0.10755 -3.822 0.000132 ***
## as.factor(DID)264 -0.44120 0.10580 -4.170 3.04e-05 ***
## as.factor(DID)266 0.61879 0.13022 4.752 2.02e-06 ***
## as.factor(DID)268 -1.12253 0.10429 -10.763 < 2e-16 ***
## as.factor(DID)275 -1.11609 0.10208 -10.933 < 2e-16 ***
## as.factor(DID)278 -0.79219 0.09862 -8.033 9.50e-16 ***
## as.factor(DID)279 -1.83698 0.10016 -18.340 < 2e-16 ***
## as.factor(DID)280 -1.05514 0.10240 -10.304 < 2e-16 ***
## as.factor(DID)281 -0.82032 0.10204 -8.039 9.05e-16 ***
## as.factor(DID)282 -0.78629 0.10925 -7.197 6.16e-13 ***
## as.factor(DID)284 -0.78193 0.10472 -7.467 8.19e-14 ***
## as.factor(DID)287 -1.04173 0.10478 -9.942 < 2e-16 ***
## as.factor(DID)289 -0.88492 0.10127 -8.738 < 2e-16 ***
## as.factor(DID)290 -1.05728 0.10444 -10.123 < 2e-16 ***
## as.factor(DID)315 -0.74213 0.11562 -6.419 1.37e-10 ***
## as.factor(DID)316 -1.09485 0.10357 -10.571 < 2e-16 ***
## as.factor(DID)318 -1.66751 0.10549 -15.807 < 2e-16 ***
## as.factor(DID)319 -2.28183 0.09897 -23.056 < 2e-16 ***
## as.factor(DID)320 -1.53198 0.10486 -14.609 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 253748 on 198147 degrees of freedom
## Residual deviance: 232377 on 198029 degrees of freedom
## AIC: 232615
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 212.13, df = 8, p-value < 2.2e-16Location dists_not_fit
ica %>%
mutate(centroid = st_centroid(geometry),
x = st_coordinates(centroid)[,1],
y = st_coordinates(centroid)[,2]) %>%
ggplot() +
geom_sf() +
geom_point(data = child_ica_dummy,
aes(x, y, color = DID %in% dists_not_fit)) +
coord_sf(xlim = c(min(child_ica_dummy$x), max(child_ica_dummy$x)),
ylim = c(min(child_ica_dummy$y), max(child_ica_dummy$y))) +
geom_text(data = child_ica_dummy %>% filter(DID == dists_not_fit),
aes(x, y, label = DID), check_overlap = TRUE, vjust = -0.5) +
ggtitle("Distrists, the Data of Which the Model Doesn't Fit Well")## Warning: Problem with `mutate()` input `centroid`.
## x st_centroid does not give correct centroids for longitude/latitude data
## i Input `centroid` is `st_centroid(geometry)`.## Warning in st_centroid.sfc(geometry): st_centroid does not give correct
## centroids for longitude/latitude data## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません## Warning: Removed 8238 rows containing missing values (geom_point).
fit the momdel for every districts using apply function
All Districts. C003_01 ~ n_children_in_household PR004_PR009_01 + C002_01
fit_all <- sapply(DID_unique, function(id){
glm_child <- glm(C003_01 ~ n_children_in_household + PR004_PR009_01 + C002_01,
family = "binomial", data = child_ica_dummy %>% filter(DID == id))
# ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)
hoslem <- hoslem.test(x = glm_child$y, y = fitted(glm_child))
data.frame(id, hoslem["p.value"])
}) %>% t()
fit_all <- fit_all %>% as.data.frame()
fit_all$id <- fit_all$id %>% as.numeric()
fit_all$p.value <- fit_all$p.value %>% as.numeric()
fit_allDistricts whose model is not fitted well
fit_all %>%
filter(p.value < 0.05)GLM on Never Enrolled
GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## PR004_PR009_01, family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.0852 -0.7787 -0.6059 -0.5696 1.9488
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.118868 0.014509 -77.115 <2e-16 ***
## n_children_in_household 0.026920 0.003095 8.699 <2e-16 ***
## C002_01 0.575131 0.009503 60.518 <2e-16 ***
## PR004_PR009_01 -0.644803 0.009532 -67.645 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 276273 on 245746 degrees of freedom
## Residual deviance: 267986 on 245743 degrees of freedom
## AIC: 267994
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 402.3, df = 8, p-value < 2.2e-16GLM C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01
glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy)
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + H002_1_01 +
## H002_2_01, family = "binomial", data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9610 -1.2179 0.6798 0.8733 1.2973
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.817833 0.015566 116.78 <2e-16 ***
## n_children_in_household -0.053166 0.002945 -18.05 <2e-16 ***
## C002_01 -0.579749 0.009071 -63.91 <2e-16 ***
## H002_1_01 -0.930780 0.012499 -74.47 <2e-16 ***
## H002_2_01 -0.257998 0.013052 -19.77 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 287756 on 245742 degrees of freedom
## AIC: 287766
##
## Number of Fisher Scoring iterations: 4## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 324.32, df = 8, p-value < 2.2e-16Hunza. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## PR004_PR009_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.3664 -0.3453 -0.3340 -0.2888 2.6065
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.39338 0.45360 -7.481 7.38e-14 ***
## n_children_in_household 0.03063 0.08648 0.354 0.723
## C002_01 0.07590 0.22551 0.337 0.736
## PR004_PR009_01 0.43561 0.30655 1.421 0.155
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 659.67 on 1609 degrees of freedom
## Residual deviance: 657.35 on 1606 degrees of freedom
## AIC: 665.35
##
## Number of Fisher Scoring iterations: 6## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 4.5829, df = 8, p-value = 0.8011Hunza. GLM C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01
glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>% filter(DID == 266))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>%
## filter(DID == 266))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.4753 -0.3872 -0.2587 -0.2500 2.6596
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.66133 0.32680 -8.144 3.83e-16 ***
## n_children_in_household 0.01893 0.08399 0.225 0.821716
## C002_01 0.07028 0.22687 0.310 0.756715
## H002_1_01 0.35399 0.32063 1.104 0.269586
## H002_2_01 -0.86483 0.25322 -3.415 0.000637 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 659.67 on 1609 degrees of freedom
## Residual deviance: 641.33 on 1605 degrees of freedom
## AIC: 651.33
##
## Number of Fisher Scoring iterations: 6## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 6.7098, df = 8, p-value = 0.5682GLM Age >= 5. GLM C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child <- glm(C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_PR009_01,
## family = "binomial", data = child_ica_dummy %>% filter(C001 >=
## 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2955 0.4095 0.5484 0.7001 1.4258
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.651863 0.017175 96.18 <2e-16 ***
## n_children_in_household -0.123601 0.003608 -34.26 <2e-16 ***
## C002_01 -0.735895 0.011276 -65.26 <2e-16 ***
## PR004_PR009_01 1.032038 0.011369 90.77 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 212206 on 208249 degrees of freedom
## Residual deviance: 198026 on 208246 degrees of freedom
## AIC: 198034
##
## Number of Fisher Scoring iterations: 4## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 96.347, df = 8, p-value < 2.2e-16GLM Age >= 5. GLM C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID)
glm_child <- glm(C003_01 ~
n_children_in_household +
C002_01 +
PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID),
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 +
## PR009_only_01 + PR004_PR009_both_01 + as.factor(DID), family = "binomial",
## data = child_ica_dummy %>% filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.3709 0.1728 0.4345 0.6912 1.8018
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.899487 0.119889 24.185 < 2e-16 ***
## n_children_in_household -0.096449 0.004058 -23.766 < 2e-16 ***
## C002_01 -0.880439 0.012053 -73.048 < 2e-16 ***
## PR004_only_01 0.372396 0.033743 11.036 < 2e-16 ***
## PR009_only_01 0.460871 0.015063 30.597 < 2e-16 ***
## PR004_PR009_both_01 0.906314 0.018700 48.465 < 2e-16 ***
## as.factor(DID)147 -0.694718 0.141050 -4.925 8.42e-07 ***
## as.factor(DID)148 -0.102451 0.148896 -0.688 0.491408
## as.factor(DID)149 -0.901388 0.135608 -6.647 2.99e-11 ***
## as.factor(DID)150 -0.793476 0.135270 -5.866 4.47e-09 ***
## as.factor(DID)151 -0.022429 0.156431 -0.143 0.885992
## as.factor(DID)152 0.039072 0.169467 0.231 0.817657
## as.factor(DID)153 -0.963592 0.137924 -6.986 2.82e-12 ***
## as.factor(DID)154 -0.757312 0.140837 -5.377 7.56e-08 ***
## as.factor(DID)155 -0.873652 0.134073 -6.516 7.21e-11 ***
## as.factor(DID)156 -1.535559 0.151893 -10.109 < 2e-16 ***
## as.factor(DID)157 -0.320648 0.151937 -2.110 0.034824 *
## as.factor(DID)158 -0.275462 0.146546 -1.880 0.060149 .
## as.factor(DID)159 -1.532667 0.140352 -10.920 < 2e-16 ***
## as.factor(DID)160 -0.862810 0.137645 -6.268 3.65e-10 ***
## as.factor(DID)161 -1.463378 0.128794 -11.362 < 2e-16 ***
## as.factor(DID)162 0.117193 0.177534 0.660 0.509180
## as.factor(DID)163 -0.168766 0.169136 -0.998 0.318369
## as.factor(DID)164 -0.913588 0.149970 -6.092 1.12e-09 ***
## as.factor(DID)165 -1.069020 0.137551 -7.772 7.74e-15 ***
## as.factor(DID)166 -0.904914 0.138992 -6.511 7.49e-11 ***
## as.factor(DID)167 0.445641 0.175981 2.532 0.011331 *
## as.factor(DID)169 -1.913391 0.129797 -14.741 < 2e-16 ***
## as.factor(DID)170 -0.431530 0.147381 -2.928 0.003412 **
## as.factor(DID)171 -0.387056 0.150422 -2.573 0.010078 *
## as.factor(DID)172 -0.553998 0.144240 -3.841 0.000123 ***
## as.factor(DID)173 -0.280344 0.146574 -1.913 0.055794 .
## as.factor(DID)174 -0.974149 0.140699 -6.924 4.40e-12 ***
## as.factor(DID)175 -1.542380 0.133271 -11.573 < 2e-16 ***
## as.factor(DID)176 0.411946 0.205760 2.002 0.045277 *
## as.factor(DID)177 -0.986479 0.135135 -7.300 2.88e-13 ***
## as.factor(DID)178 0.729015 0.185628 3.927 8.59e-05 ***
## as.factor(DID)179 -0.337996 0.149653 -2.259 0.023913 *
## as.factor(DID)180 -0.640184 0.141685 -4.518 6.23e-06 ***
## as.factor(DID)181 -0.339986 0.152590 -2.228 0.025874 *
## as.factor(DID)182 -1.587793 0.131594 -12.066 < 2e-16 ***
## as.factor(DID)183 -0.752766 0.140822 -5.346 9.02e-08 ***
## as.factor(DID)184 -1.380532 0.131473 -10.500 < 2e-16 ***
## as.factor(DID)185 0.580322 0.160995 3.605 0.000313 ***
## as.factor(DID)186 -1.604095 0.131283 -12.219 < 2e-16 ***
## as.factor(DID)187 -1.384015 0.133458 -10.370 < 2e-16 ***
## as.factor(DID)188 -1.154858 0.134257 -8.602 < 2e-16 ***
## as.factor(DID)189 -0.297516 0.144937 -2.053 0.040100 *
## as.factor(DID)190 -1.008322 0.143109 -7.046 1.84e-12 ***
## as.factor(DID)191 -0.880028 0.143283 -6.142 8.15e-10 ***
## as.factor(DID)192 -0.581270 0.145776 -3.987 6.68e-05 ***
## as.factor(DID)193 0.194334 0.149756 1.298 0.194399
## as.factor(DID)194 -1.833927 0.130417 -14.062 < 2e-16 ***
## as.factor(DID)195 -2.043092 0.131591 -15.526 < 2e-16 ***
## as.factor(DID)196 -0.190307 0.164257 -1.159 0.246623
## as.factor(DID)197 -1.567823 0.131772 -11.898 < 2e-16 ***
## as.factor(DID)198 -1.950151 0.130308 -14.966 < 2e-16 ***
## as.factor(DID)199 1.316189 0.190052 6.925 4.35e-12 ***
## as.factor(DID)200 -1.577631 0.134445 -11.734 < 2e-16 ***
## as.factor(DID)202 -1.577230 0.133171 -11.844 < 2e-16 ***
## as.factor(DID)203 -1.696145 0.129359 -13.112 < 2e-16 ***
## as.factor(DID)204 -0.789079 0.137557 -5.736 9.67e-09 ***
## as.factor(DID)205 -1.779512 0.129027 -13.792 < 2e-16 ***
## as.factor(DID)206 -1.652202 0.129155 -12.792 < 2e-16 ***
## as.factor(DID)207 -2.149577 0.129544 -16.593 < 2e-16 ***
## as.factor(DID)208 -1.519791 0.130857 -11.614 < 2e-16 ***
## as.factor(DID)209 -0.879885 0.144756 -6.078 1.21e-09 ***
## as.factor(DID)210 -1.784615 0.128359 -13.903 < 2e-16 ***
## as.factor(DID)211 -1.012345 0.142460 -7.106 1.19e-12 ***
## as.factor(DID)212 -1.593997 0.131690 -12.104 < 2e-16 ***
## as.factor(DID)213 -1.653284 0.129759 -12.741 < 2e-16 ***
## as.factor(DID)214 -2.054347 0.130051 -15.796 < 2e-16 ***
## as.factor(DID)215 -1.057076 0.129511 -8.162 3.29e-16 ***
## as.factor(DID)216 -1.899444 0.131809 -14.411 < 2e-16 ***
## as.factor(DID)217 -1.722100 0.131150 -13.131 < 2e-16 ***
## as.factor(DID)218 -1.713569 0.128130 -13.374 < 2e-16 ***
## as.factor(DID)219 -1.758501 0.131643 -13.358 < 2e-16 ***
## as.factor(DID)220 -2.086986 0.126605 -16.484 < 2e-16 ***
## as.factor(DID)221 -2.554577 0.130914 -19.513 < 2e-16 ***
## as.factor(DID)222 -1.868063 0.128090 -14.584 < 2e-16 ***
## as.factor(DID)223 -1.533269 0.139598 -10.983 < 2e-16 ***
## as.factor(DID)224 -2.167135 0.129893 -16.684 < 2e-16 ***
## as.factor(DID)225 -2.111673 0.130464 -16.186 < 2e-16 ***
## as.factor(DID)226 -1.477990 0.133501 -11.071 < 2e-16 ***
## as.factor(DID)227 -1.591459 0.128905 -12.346 < 2e-16 ***
## as.factor(DID)228 -2.540654 0.131996 -19.248 < 2e-16 ***
## as.factor(DID)229 -1.854078 0.132873 -13.954 < 2e-16 ***
## as.factor(DID)230 -2.185344 0.130044 -16.805 < 2e-16 ***
## as.factor(DID)231 -1.679584 0.129106 -13.009 < 2e-16 ***
## as.factor(DID)232 -0.407614 0.141104 -2.889 0.003868 **
## as.factor(DID)233 -2.526160 0.133969 -18.856 < 2e-16 ***
## as.factor(DID)234 -1.544160 0.137621 -11.220 < 2e-16 ***
## as.factor(DID)235 -0.878094 0.136064 -6.454 1.09e-10 ***
## as.factor(DID)236 -1.028567 0.137486 -7.481 7.36e-14 ***
## as.factor(DID)237 -0.623776 0.142433 -4.379 1.19e-05 ***
## as.factor(DID)238 2.161672 0.278170 7.771 7.78e-15 ***
## as.factor(DID)239 -0.230980 0.147443 -1.567 0.117214
## as.factor(DID)240 -0.943068 0.138349 -6.817 9.32e-12 ***
## as.factor(DID)241 -2.018914 0.130733 -15.443 < 2e-16 ***
## as.factor(DID)242 -0.974203 0.137943 -7.062 1.64e-12 ***
## as.factor(DID)243 1.145910 0.286737 3.996 6.43e-05 ***
## as.factor(DID)244 -0.481463 0.151248 -3.183 0.001456 **
## as.factor(DID)245 0.500859 0.176576 2.837 0.004561 **
## as.factor(DID)246 -1.621224 0.133290 -12.163 < 2e-16 ***
## as.factor(DID)247 -1.320995 0.140180 -9.424 < 2e-16 ***
## as.factor(DID)248 -0.876565 0.132441 -6.619 3.63e-11 ***
## as.factor(DID)249 0.175049 0.169357 1.034 0.301320
## as.factor(DID)250 -0.673176 0.138847 -4.848 1.25e-06 ***
## as.factor(DID)251 -0.507268 0.144643 -3.507 0.000453 ***
## as.factor(DID)252 -0.844213 0.138085 -6.114 9.73e-10 ***
## as.factor(DID)253 -0.599220 0.147988 -4.049 5.14e-05 ***
## as.factor(DID)254 -1.750979 0.150685 -11.620 < 2e-16 ***
## as.factor(DID)255 -0.901385 0.144889 -6.221 4.93e-10 ***
## as.factor(DID)256 0.221349 0.152148 1.455 0.145719
## as.factor(DID)257 0.304660 0.191405 1.592 0.111452
## as.factor(DID)258 -1.778704 0.130221 -13.659 < 2e-16 ***
## as.factor(DID)259 0.797673 0.195064 4.089 4.33e-05 ***
## as.factor(DID)260 0.270849 0.153340 1.766 0.077342 .
## as.factor(DID)261 -2.266533 0.128398 -17.652 < 2e-16 ***
## as.factor(DID)262 -0.302815 0.143168 -2.115 0.034420 *
## as.factor(DID)263 0.213236 0.156933 1.359 0.174221
## as.factor(DID)264 0.038498 0.148922 0.259 0.796016
## as.factor(DID)265 0.690491 0.178925 3.859 0.000114 ***
## as.factor(DID)266 0.931776 0.195706 4.761 1.93e-06 ***
## as.factor(DID)267 0.650586 0.174413 3.730 0.000191 ***
## as.factor(DID)268 -0.786670 0.148931 -5.282 1.28e-07 ***
## as.factor(DID)269 1.776172 0.285555 6.220 4.97e-10 ***
## as.factor(DID)270 0.689285 0.172000 4.007 6.14e-05 ***
## as.factor(DID)271 1.341308 0.237725 5.642 1.68e-08 ***
## as.factor(DID)272 1.753460 0.278089 6.305 2.87e-10 ***
## as.factor(DID)273 0.580839 0.185768 3.127 0.001768 **
## as.factor(DID)274 1.021699 0.250745 4.075 4.61e-05 ***
## as.factor(DID)275 -1.036634 0.138275 -7.497 6.53e-14 ***
## as.factor(DID)276 2.105320 0.277851 7.577 3.53e-14 ***
## as.factor(DID)277 -0.269683 0.203955 -1.322 0.186078
## as.factor(DID)278 -0.375287 0.133932 -2.802 0.005077 **
## as.factor(DID)279 -1.770222 0.131573 -13.454 < 2e-16 ***
## as.factor(DID)280 -0.668344 0.141198 -4.733 2.21e-06 ***
## as.factor(DID)281 -0.009726 0.149903 -0.065 0.948266
## as.factor(DID)282 -0.496916 0.151684 -3.276 0.001053 **
## as.factor(DID)284 -0.713572 0.138794 -5.141 2.73e-07 ***
## as.factor(DID)287 -0.698540 0.144448 -4.836 1.33e-06 ***
## as.factor(DID)289 -1.070184 0.132693 -8.065 7.32e-16 ***
## as.factor(DID)290 -0.995415 0.138011 -7.213 5.49e-13 ***
## as.factor(DID)315 -0.581439 0.162863 -3.570 0.000357 ***
## as.factor(DID)316 -1.327520 0.134923 -9.839 < 2e-16 ***
## as.factor(DID)318 -1.795890 0.135705 -13.234 < 2e-16 ***
## as.factor(DID)319 -2.452244 0.128858 -19.031 < 2e-16 ***
## as.factor(DID)320 -1.793792 0.135743 -13.215 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 212206 on 208249 degrees of freedom
## Residual deviance: 177115 on 208101 degrees of freedom
## AIC: 177413
##
## Number of Fisher Scoring iterations: 7## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 102.52, df = 8, p-value < 2.2e-16AIC
extractAIC(glm_child)## [1] 149.0 177412.6BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 149.0 178939.3effectiveness of explanatory variables
glm_child_null <- glm(C003_01 ~ 1, family = "binomial",
data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")multicolinearity
vif(glm_child)## GVIF Df GVIF^(1/(2*Df))
## n_children_in_household 1.148529 1 1.071694
## C002_01 1.032871 1 1.016302
## PR004_only_01 1.049785 1 1.024590
## PR009_only_01 1.211205 1 1.100548
## PR004_PR009_both_01 1.310188 1 1.144634
## as.factor(DID) 1.542294 143 1.001516GLM Age >= 5. GLM C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(C001) + as.factor(DID)
glm_child <- glm(C003_01 ~
n_children_in_household +
C002_01 +
PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 +
as.factor(C001) + as.factor(DID),
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_01 ~ n_children_in_household + C002_01 + PR004_only_01 +
## PR009_only_01 + PR004_PR009_both_01 + as.factor(C001) + as.factor(DID),
## family = "binomial", data = child_ica_dummy %>% filter(C001 >=
## 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.4751 0.1470 0.3916 0.6568 2.2667
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.236346 0.122653 18.233 < 2e-16 ***
## n_children_in_household -0.106627 0.004158 -25.644 < 2e-16 ***
## C002_01 -0.930098 0.012449 -74.712 < 2e-16 ***
## PR004_only_01 0.387283 0.034732 11.150 < 2e-16 ***
## PR009_only_01 0.478216 0.015487 30.878 < 2e-16 ***
## PR004_PR009_both_01 0.942980 0.019174 49.180 < 2e-16 ***
## as.factor(C001)6 0.838229 0.024771 33.840 < 2e-16 ***
## as.factor(C001)7 1.324418 0.026334 50.294 < 2e-16 ***
## as.factor(C001)8 1.428928 0.026253 54.429 < 2e-16 ***
## as.factor(C001)9 1.495093 0.028918 51.702 < 2e-16 ***
## as.factor(C001)10 1.301743 0.025936 50.191 < 2e-16 ***
## as.factor(C001)11 1.201043 0.031233 38.454 < 2e-16 ***
## as.factor(C001)12 0.920281 0.027075 33.990 < 2e-16 ***
## as.factor(C001)13 0.689955 0.028503 24.207 < 2e-16 ***
## as.factor(C001)14 0.381012 0.027060 14.080 < 2e-16 ***
## as.factor(C001)15 0.161030 0.029101 5.534 3.14e-08 ***
## as.factor(C001)16 -0.089958 0.027385 -3.285 0.001020 **
## as.factor(DID)147 -0.784418 0.143471 -5.467 4.57e-08 ***
## as.factor(DID)148 -0.093647 0.151372 -0.619 0.536144
## as.factor(DID)149 -0.981176 0.138000 -7.110 1.16e-12 ***
## as.factor(DID)150 -0.837673 0.137748 -6.081 1.19e-09 ***
## as.factor(DID)151 -0.092873 0.158868 -0.585 0.558824
## as.factor(DID)152 0.031874 0.171944 0.185 0.852937
## as.factor(DID)153 -1.028301 0.140457 -7.321 2.46e-13 ***
## as.factor(DID)154 -0.807692 0.143409 -5.632 1.78e-08 ***
## as.factor(DID)155 -0.951648 0.136528 -6.970 3.16e-12 ***
## as.factor(DID)156 -1.666966 0.155141 -10.745 < 2e-16 ***
## as.factor(DID)157 -0.348356 0.154313 -2.257 0.023979 *
## as.factor(DID)158 -0.309890 0.149030 -2.079 0.037582 *
## as.factor(DID)159 -1.664872 0.142856 -11.654 < 2e-16 ***
## as.factor(DID)160 -0.914787 0.140183 -6.526 6.77e-11 ***
## as.factor(DID)161 -1.539884 0.131147 -11.742 < 2e-16 ***
## as.factor(DID)162 0.078467 0.179825 0.436 0.662582
## as.factor(DID)163 -0.219480 0.171823 -1.277 0.201475
## as.factor(DID)164 -0.988283 0.152603 -6.476 9.41e-11 ***
## as.factor(DID)165 -1.142611 0.140204 -8.150 3.65e-16 ***
## as.factor(DID)166 -1.064827 0.141448 -7.528 5.15e-14 ***
## as.factor(DID)167 0.451268 0.178252 2.532 0.011354 *
## as.factor(DID)169 -2.042023 0.132290 -15.436 < 2e-16 ***
## as.factor(DID)170 -0.461501 0.150011 -3.076 0.002095 **
## as.factor(DID)171 -0.383486 0.152999 -2.506 0.012195 *
## as.factor(DID)172 -0.615547 0.146642 -4.198 2.70e-05 ***
## as.factor(DID)173 -0.310071 0.148937 -2.082 0.037352 *
## as.factor(DID)174 -1.056767 0.143288 -7.375 1.64e-13 ***
## as.factor(DID)175 -1.672568 0.135814 -12.315 < 2e-16 ***
## as.factor(DID)176 0.391751 0.207912 1.884 0.059536 .
## as.factor(DID)177 -1.056060 0.137646 -7.672 1.69e-14 ***
## as.factor(DID)178 0.748058 0.187649 3.986 6.71e-05 ***
## as.factor(DID)179 -0.365307 0.152115 -2.402 0.016327 *
## as.factor(DID)180 -0.676729 0.144323 -4.689 2.75e-06 ***
## as.factor(DID)181 -0.284123 0.154858 -1.835 0.066546 .
## as.factor(DID)182 -1.743768 0.134112 -13.002 < 2e-16 ***
## as.factor(DID)183 -0.725167 0.143396 -5.057 4.26e-07 ***
## as.factor(DID)184 -1.461445 0.134038 -10.903 < 2e-16 ***
## as.factor(DID)185 0.410339 0.163317 2.513 0.011987 *
## as.factor(DID)186 -1.699844 0.133863 -12.698 < 2e-16 ***
## as.factor(DID)187 -1.530893 0.136151 -11.244 < 2e-16 ***
## as.factor(DID)188 -1.247158 0.136706 -9.123 < 2e-16 ***
## as.factor(DID)189 -0.435398 0.147278 -2.956 0.003114 **
## as.factor(DID)190 -1.037103 0.145935 -7.107 1.19e-12 ***
## as.factor(DID)191 -0.928599 0.146031 -6.359 2.03e-10 ***
## as.factor(DID)192 -0.605415 0.148511 -4.077 4.57e-05 ***
## as.factor(DID)193 0.013415 0.152164 0.088 0.929746
## as.factor(DID)194 -1.891979 0.132877 -14.239 < 2e-16 ***
## as.factor(DID)195 -2.160590 0.134138 -16.107 < 2e-16 ***
## as.factor(DID)196 -0.252910 0.166684 -1.517 0.129191
## as.factor(DID)197 -1.687335 0.134251 -12.569 < 2e-16 ***
## as.factor(DID)198 -2.122290 0.132840 -15.976 < 2e-16 ***
## as.factor(DID)199 1.282946 0.192131 6.677 2.43e-11 ***
## as.factor(DID)200 -1.710652 0.137128 -12.475 < 2e-16 ***
## as.factor(DID)202 -1.719709 0.135765 -12.667 < 2e-16 ***
## as.factor(DID)203 -1.849364 0.131847 -14.027 < 2e-16 ***
## as.factor(DID)204 -0.841368 0.140185 -6.002 1.95e-09 ***
## as.factor(DID)205 -1.893421 0.131421 -14.407 < 2e-16 ***
## as.factor(DID)206 -1.847546 0.131596 -14.040 < 2e-16 ***
## as.factor(DID)207 -2.314375 0.131985 -17.535 < 2e-16 ***
## as.factor(DID)208 -1.621990 0.133352 -12.163 < 2e-16 ***
## as.factor(DID)209 -0.946120 0.147759 -6.403 1.52e-10 ***
## as.factor(DID)210 -1.916745 0.130726 -14.662 < 2e-16 ***
## as.factor(DID)211 -1.116472 0.145225 -7.688 1.50e-14 ***
## as.factor(DID)212 -1.714244 0.134122 -12.781 < 2e-16 ***
## as.factor(DID)213 -1.775314 0.132174 -13.432 < 2e-16 ***
## as.factor(DID)214 -2.233748 0.132613 -16.844 < 2e-16 ***
## as.factor(DID)215 -1.181603 0.132030 -8.949 < 2e-16 ***
## as.factor(DID)216 -2.069586 0.134391 -15.400 < 2e-16 ***
## as.factor(DID)217 -1.806066 0.133744 -13.504 < 2e-16 ***
## as.factor(DID)218 -1.880252 0.130511 -14.407 < 2e-16 ***
## as.factor(DID)219 -1.900769 0.134133 -14.171 < 2e-16 ***
## as.factor(DID)220 -2.227067 0.128999 -17.264 < 2e-16 ***
## as.factor(DID)221 -2.745910 0.133357 -20.591 < 2e-16 ***
## as.factor(DID)222 -1.993950 0.130488 -15.281 < 2e-16 ***
## as.factor(DID)223 -1.603530 0.142620 -11.243 < 2e-16 ***
## as.factor(DID)224 -2.287773 0.132287 -17.294 < 2e-16 ***
## as.factor(DID)225 -2.276112 0.132907 -17.126 < 2e-16 ***
## as.factor(DID)226 -1.564626 0.136238 -11.485 < 2e-16 ***
## as.factor(DID)227 -1.735005 0.131345 -13.210 < 2e-16 ***
## as.factor(DID)228 -2.658627 0.134570 -19.756 < 2e-16 ***
## as.factor(DID)229 -2.002510 0.135447 -14.784 < 2e-16 ***
## as.factor(DID)230 -2.316566 0.132521 -17.481 < 2e-16 ***
## as.factor(DID)231 -1.754156 0.131544 -13.335 < 2e-16 ***
## as.factor(DID)232 -0.464304 0.143636 -3.233 0.001227 **
## as.factor(DID)233 -2.703242 0.136577 -19.793 < 2e-16 ***
## as.factor(DID)234 -1.713581 0.140235 -12.219 < 2e-16 ***
## as.factor(DID)235 -0.893286 0.138547 -6.448 1.14e-10 ***
## as.factor(DID)236 -1.030341 0.139964 -7.361 1.82e-13 ***
## as.factor(DID)237 -0.650486 0.144931 -4.488 7.18e-06 ***
## as.factor(DID)238 2.074160 0.279477 7.422 1.16e-13 ***
## as.factor(DID)239 -0.195311 0.150059 -1.302 0.193068
## as.factor(DID)240 -0.991210 0.140867 -7.036 1.97e-12 ***
## as.factor(DID)241 -2.176730 0.133347 -16.324 < 2e-16 ***
## as.factor(DID)242 -1.056696 0.140562 -7.518 5.58e-14 ***
## as.factor(DID)243 1.248233 0.288443 4.327 1.51e-05 ***
## as.factor(DID)244 -0.458364 0.153775 -2.981 0.002875 **
## as.factor(DID)245 0.499207 0.178728 2.793 0.005220 **
## as.factor(DID)246 -1.743685 0.135781 -12.842 < 2e-16 ***
## as.factor(DID)247 -1.333756 0.143196 -9.314 < 2e-16 ***
## as.factor(DID)248 -0.907417 0.134861 -6.729 1.71e-11 ***
## as.factor(DID)249 0.158783 0.171813 0.924 0.355402
## as.factor(DID)250 -0.712523 0.141296 -5.043 4.59e-07 ***
## as.factor(DID)251 -0.565760 0.147171 -3.844 0.000121 ***
## as.factor(DID)252 -0.898395 0.140523 -6.393 1.62e-10 ***
## as.factor(DID)253 -0.621309 0.150444 -4.130 3.63e-05 ***
## as.factor(DID)254 -1.790591 0.153669 -11.652 < 2e-16 ***
## as.factor(DID)255 -0.935352 0.147504 -6.341 2.28e-10 ***
## as.factor(DID)256 0.207917 0.154532 1.345 0.178477
## as.factor(DID)257 0.344889 0.193597 1.781 0.074835 .
## as.factor(DID)258 -1.948788 0.132914 -14.662 < 2e-16 ***
## as.factor(DID)259 0.759450 0.197304 3.849 0.000119 ***
## as.factor(DID)260 0.310348 0.155598 1.995 0.046092 *
## as.factor(DID)261 -2.433519 0.130869 -18.595 < 2e-16 ***
## as.factor(DID)262 -0.326379 0.145632 -2.241 0.025018 *
## as.factor(DID)263 0.263813 0.159259 1.657 0.097620 .
## as.factor(DID)264 0.073832 0.151384 0.488 0.625751
## as.factor(DID)265 0.762879 0.181253 4.209 2.57e-05 ***
## as.factor(DID)266 1.003127 0.197894 5.069 4.00e-07 ***
## as.factor(DID)267 0.666027 0.176493 3.774 0.000161 ***
## as.factor(DID)268 -0.790351 0.151366 -5.221 1.78e-07 ***
## as.factor(DID)269 1.810794 0.286952 6.310 2.78e-10 ***
## as.factor(DID)270 0.686298 0.174083 3.942 8.07e-05 ***
## as.factor(DID)271 1.364386 0.239319 5.701 1.19e-08 ***
## as.factor(DID)272 1.768055 0.279472 6.326 2.51e-10 ***
## as.factor(DID)273 0.630907 0.187810 3.359 0.000781 ***
## as.factor(DID)274 1.026168 0.252575 4.063 4.85e-05 ***
## as.factor(DID)275 -0.974477 0.140893 -6.916 4.63e-12 ***
## as.factor(DID)276 2.180771 0.279161 7.812 5.63e-15 ***
## as.factor(DID)277 -0.317574 0.206789 -1.536 0.124603
## as.factor(DID)278 -0.451825 0.136262 -3.316 0.000914 ***
## as.factor(DID)279 -1.922434 0.134162 -14.329 < 2e-16 ***
## as.factor(DID)280 -0.736505 0.143713 -5.125 2.98e-07 ***
## as.factor(DID)281 0.019798 0.152403 0.130 0.896640
## as.factor(DID)282 -0.670133 0.154367 -4.341 1.42e-05 ***
## as.factor(DID)284 -0.722366 0.141257 -5.114 3.16e-07 ***
## as.factor(DID)287 -0.692193 0.147107 -4.705 2.53e-06 ***
## as.factor(DID)289 -1.038543 0.135358 -7.673 1.69e-14 ***
## as.factor(DID)290 -1.134186 0.140658 -8.063 7.42e-16 ***
## as.factor(DID)315 -0.668540 0.165471 -4.040 5.34e-05 ***
## as.factor(DID)316 -1.529865 0.137377 -11.136 < 2e-16 ***
## as.factor(DID)318 -1.901524 0.138526 -13.727 < 2e-16 ***
## as.factor(DID)319 -2.613872 0.131260 -19.914 < 2e-16 ***
## as.factor(DID)320 -1.944721 0.138241 -14.068 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 212206 on 208249 degrees of freedom
## Residual deviance: 168677 on 208090 degrees of freedom
## AIC: 168997
##
## Number of Fisher Scoring iterations: 7## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 22.909, df = 8, p-value = 0.003483AIC
extractAIC(glm_child)## [1] 160.0 168997.3BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 160.0 170636.7effectiveness of explanatory variables
glm_child_null <- glm(C003_01 ~ 1, family = "binomial",
data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")multicolinearity
vif(glm_child)## GVIF Df GVIF^(1/(2*Df))
## n_children_in_household 1.155403 1 1.074897
## C002_01 1.043050 1 1.021298
## PR004_only_01 1.049989 1 1.024690
## PR009_only_01 1.214044 1 1.101837
## PR004_PR009_both_01 1.316493 1 1.147385
## as.factor(C001) 1.105681 11 1.004577
## as.factor(DID) 1.661579 143 1.001777GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + H002
glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## H002_1_01 + H002_2_01, family = "binomial", data = child_ica_dummy %>%
## filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1606 -0.6023 -0.4740 -0.3346 2.5325
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.267561 0.024107 -135.54 <2e-16 ***
## n_children_in_household 0.102019 0.004052 25.18 <2e-16 ***
## C002_01 0.831929 0.012823 64.88 <2e-16 ***
## H002_1_01 1.273672 0.019170 66.44 <2e-16 ***
## H002_2_01 0.310590 0.020780 14.95 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175563 on 208249 degrees of freedom
## Residual deviance: 163262 on 208245 degrees of freedom
## AIC: 163272
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 104.14, df = 8, p-value < 2.2e-16GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01
glm_child <- glm(C003_1_01 ~ n_children_in_household + C002_01 + PR004_PR009_01, family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## PR004_PR009_01, family = "binomial", data = child_ica_dummy %>%
## filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1965 -0.5924 -0.5127 -0.3458 2.4672
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.025765 0.019446 -104.17 <2e-16 ***
## n_children_in_household 0.103873 0.004048 25.66 <2e-16 ***
## C002_01 0.824116 0.012805 64.36 <2e-16 ***
## PR004_PR009_01 -1.072905 0.013003 -82.51 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175563 on 208249 degrees of freedom
## Residual deviance: 163492 on 208246 degrees of freedom
## AIC: 163500
##
## Number of Fisher Scoring iterations: 5## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
## Warning in eval(family$initialize): non-integer #successes in a binomial glm!
Hosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 62.991, df = 8, p-value = 1.203e-10GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01
glm_child <- glm(C003_1_01 ~
n_children_in_household +
C002_01 +
PR004_only_01 + PR009_only_01 + PR004_PR009_both_01,
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## PR004_only_01 + PR009_only_01 + PR004_PR009_both_01, family = "binomial",
## data = child_ica_dummy %>% filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.1573 -0.6019 -0.4316 -0.2874 2.6351
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.982321 0.019437 -101.99 <2e-16 ***
## n_children_in_household 0.091439 0.004062 22.51 <2e-16 ***
## C002_01 0.837617 0.012837 65.25 <2e-16 ***
## PR004_only_01 -0.618710 0.036280 -17.05 <2e-16 ***
## PR009_only_01 -0.755745 0.015594 -48.46 <2e-16 ***
## PR004_PR009_both_01 -1.549333 0.019266 -80.42 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175563 on 208249 degrees of freedom
## Residual deviance: 161951 on 208244 degrees of freedom
## AIC: 161963
##
## Number of Fisher Scoring iterations: 5## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 168.55, df = 8, p-value < 2.2e-16GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID) +as.factor(C001)
glm_child <- glm(C003_1_01 ~
n_children_in_household +
C002_01 +
PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 +
as.factor(DID) + as.factor(C001),
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID) +
## as.factor(C001), family = "binomial", data = child_ica_dummy %>%
## filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2017 -0.5564 -0.3235 -0.1480 3.6277
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.849141 0.133244 -13.878 < 2e-16 ***
## n_children_in_household 0.103958 0.004664 22.290 < 2e-16 ***
## C002_01 0.999144 0.014066 71.034 < 2e-16 ***
## PR004_only_01 -0.403175 0.039899 -10.105 < 2e-16 ***
## PR009_only_01 -0.532623 0.017797 -29.927 < 2e-16 ***
## PR004_PR009_both_01 -0.963714 0.022615 -42.614 < 2e-16 ***
## as.factor(DID)147 0.205564 0.164266 1.251 0.210785
## as.factor(DID)148 -0.828939 0.194950 -4.252 2.12e-05 ***
## as.factor(DID)149 0.168422 0.160527 1.049 0.294094
## as.factor(DID)150 0.023630 0.159293 0.148 0.882071
## as.factor(DID)151 -0.665946 0.199491 -3.338 0.000843 ***
## as.factor(DID)152 -0.298050 0.197139 -1.512 0.130566
## as.factor(DID)153 0.497344 0.158172 3.144 0.001665 **
## as.factor(DID)154 -0.033152 0.172603 -0.192 0.847685
## as.factor(DID)155 0.192964 0.156074 1.236 0.216325
## as.factor(DID)156 1.614906 0.166834 9.680 < 2e-16 ***
## as.factor(DID)157 -0.301365 0.185305 -1.626 0.103881
## as.factor(DID)158 -0.773480 0.196681 -3.933 8.40e-05 ***
## as.factor(DID)159 1.571675 0.154255 10.189 < 2e-16 ***
## as.factor(DID)160 0.392209 0.157431 2.491 0.012727 *
## as.factor(DID)161 1.195362 0.143193 8.348 < 2e-16 ***
## as.factor(DID)162 -0.276268 0.202542 -1.364 0.172567
## as.factor(DID)163 -0.222917 0.202380 -1.101 0.270689
## as.factor(DID)164 0.770335 0.168918 4.560 5.11e-06 ***
## as.factor(DID)165 0.697253 0.156374 4.459 8.24e-06 ***
## as.factor(DID)166 -0.540689 0.186512 -2.899 0.003744 **
## as.factor(DID)167 -1.077479 0.228114 -4.723 2.32e-06 ***
## as.factor(DID)169 1.797122 0.142723 12.592 < 2e-16 ***
## as.factor(DID)170 0.116329 0.168088 0.692 0.488892
## as.factor(DID)171 -0.167964 0.180671 -0.930 0.352544
## as.factor(DID)172 -0.188000 0.174971 -1.074 0.282615
## as.factor(DID)173 -0.184565 0.172638 -1.069 0.285031
## as.factor(DID)174 0.421502 0.162740 2.590 0.009597 **
## as.factor(DID)175 1.206286 0.149076 8.092 5.88e-16 ***
## as.factor(DID)176 -1.580346 0.320298 -4.934 8.06e-07 ***
## as.factor(DID)177 0.235226 0.158818 1.481 0.138578
## as.factor(DID)178 -2.532112 0.378289 -6.694 2.18e-11 ***
## as.factor(DID)179 -0.603133 0.191250 -3.154 0.001612 **
## as.factor(DID)180 0.089222 0.165482 0.539 0.589774
## as.factor(DID)181 -1.537288 0.278060 -5.529 3.23e-08 ***
## as.factor(DID)182 1.451183 0.145476 9.975 < 2e-16 ***
## as.factor(DID)183 0.408925 0.160516 2.548 0.010848 *
## as.factor(DID)184 1.287504 0.145110 8.873 < 2e-16 ***
## as.factor(DID)185 -2.373217 0.291999 -8.127 4.38e-16 ***
## as.factor(DID)186 1.598946 0.144219 11.087 < 2e-16 ***
## as.factor(DID)187 1.545379 0.145880 10.593 < 2e-16 ***
## as.factor(DID)188 1.082158 0.148089 7.307 2.72e-13 ***
## as.factor(DID)189 0.123719 0.163652 0.756 0.449656
## as.factor(DID)190 0.984796 0.157629 6.248 4.17e-10 ***
## as.factor(DID)191 0.448639 0.164781 2.723 0.006476 **
## as.factor(DID)192 0.223198 0.163943 1.361 0.173376
## as.factor(DID)193 -0.942345 0.189106 -4.983 6.26e-07 ***
## as.factor(DID)194 1.549331 0.144193 10.745 < 2e-16 ***
## as.factor(DID)195 2.058681 0.144074 14.289 < 2e-16 ***
## as.factor(DID)196 -0.482149 0.202255 -2.384 0.017132 *
## as.factor(DID)197 1.358136 0.146099 9.296 < 2e-16 ***
## as.factor(DID)198 1.901734 0.143289 13.272 < 2e-16 ***
## as.factor(DID)199 -1.898863 0.230299 -8.245 < 2e-16 ***
## as.factor(DID)200 1.645844 0.147095 11.189 < 2e-16 ***
## as.factor(DID)202 0.972419 0.151301 6.427 1.30e-10 ***
## as.factor(DID)203 1.600055 0.142738 11.210 < 2e-16 ***
## as.factor(DID)204 0.367293 0.155241 2.366 0.017984 *
## as.factor(DID)205 1.545918 0.143079 10.805 < 2e-16 ***
## as.factor(DID)206 1.318598 0.143909 9.163 < 2e-16 ***
## as.factor(DID)207 2.006045 0.142147 14.112 < 2e-16 ***
## as.factor(DID)208 1.100007 0.146457 7.511 5.88e-14 ***
## as.factor(DID)209 0.642938 0.160908 3.996 6.45e-05 ***
## as.factor(DID)210 1.591918 0.141432 11.256 < 2e-16 ***
## as.factor(DID)211 0.724967 0.160080 4.529 5.93e-06 ***
## as.factor(DID)212 1.303122 0.146545 8.892 < 2e-16 ***
## as.factor(DID)213 1.230974 0.144657 8.510 < 2e-16 ***
## as.factor(DID)214 1.749593 0.143375 12.203 < 2e-16 ***
## as.factor(DID)215 0.639540 0.145059 4.409 1.04e-05 ***
## as.factor(DID)216 1.823115 0.145175 12.558 < 2e-16 ***
## as.factor(DID)217 1.567133 0.144493 10.846 < 2e-16 ***
## as.factor(DID)218 1.630086 0.141505 11.520 < 2e-16 ***
## as.factor(DID)219 1.404646 0.146425 9.593 < 2e-16 ***
## as.factor(DID)220 1.859494 0.139248 13.354 < 2e-16 ***
## as.factor(DID)221 2.110932 0.143199 14.741 < 2e-16 ***
## as.factor(DID)222 1.527495 0.141950 10.761 < 2e-16 ***
## as.factor(DID)223 1.193202 0.156236 7.637 2.22e-14 ***
## as.factor(DID)224 1.810767 0.143197 12.645 < 2e-16 ***
## as.factor(DID)225 1.505207 0.145868 10.319 < 2e-16 ***
## as.factor(DID)226 1.481510 0.146355 10.123 < 2e-16 ***
## as.factor(DID)227 1.019017 0.143777 7.087 1.37e-12 ***
## as.factor(DID)228 2.404917 0.143965 16.705 < 2e-16 ***
## as.factor(DID)229 1.545111 0.146713 10.532 < 2e-16 ***
## as.factor(DID)230 1.960398 0.143007 13.708 < 2e-16 ***
## as.factor(DID)231 1.268291 0.143591 8.833 < 2e-16 ***
## as.factor(DID)232 0.209344 0.157984 1.325 0.185138
## as.factor(DID)233 2.212538 0.146746 15.077 < 2e-16 ***
## as.factor(DID)234 1.463183 0.151296 9.671 < 2e-16 ***
## as.factor(DID)235 0.015099 0.162894 0.093 0.926149
## as.factor(DID)236 0.579297 0.155809 3.718 0.000201 ***
## as.factor(DID)237 0.378634 0.160412 2.360 0.018256 *
## as.factor(DID)238 -2.197426 0.308821 -7.116 1.11e-12 ***
## as.factor(DID)239 -1.265875 0.207622 -6.097 1.08e-09 ***
## as.factor(DID)240 0.766126 0.153751 4.983 6.26e-07 ***
## as.factor(DID)241 1.176999 0.145743 8.076 6.70e-16 ***
## as.factor(DID)242 0.899382 0.152594 5.894 3.77e-09 ***
## as.factor(DID)243 -1.845110 0.380184 -4.853 1.21e-06 ***
## as.factor(DID)244 0.157954 0.174603 0.905 0.365651
## as.factor(DID)245 -0.447228 0.192528 -2.323 0.020183 *
## as.factor(DID)246 1.503046 0.147043 10.222 < 2e-16 ***
## as.factor(DID)247 1.326002 0.153763 8.624 < 2e-16 ***
## as.factor(DID)248 0.362154 0.150207 2.411 0.015907 *
## as.factor(DID)249 -0.374297 0.193624 -1.933 0.053222 .
## as.factor(DID)250 0.523122 0.154500 3.386 0.000709 ***
## as.factor(DID)251 0.455282 0.160349 2.839 0.004521 **
## as.factor(DID)252 0.740172 0.153473 4.823 1.42e-06 ***
## as.factor(DID)253 -0.238781 0.188180 -1.269 0.204477
## as.factor(DID)254 1.707990 0.164425 10.388 < 2e-16 ***
## as.factor(DID)255 0.690543 0.163470 4.224 2.40e-05 ***
## as.factor(DID)256 -1.012697 0.195881 -5.170 2.34e-07 ***
## as.factor(DID)257 -0.582009 0.220294 -2.642 0.008243 **
## as.factor(DID)258 1.385600 0.146101 9.484 < 2e-16 ***
## as.factor(DID)259 -1.010251 0.213464 -4.733 2.22e-06 ***
## as.factor(DID)260 -0.423237 0.171080 -2.474 0.013364 *
## as.factor(DID)261 2.453180 0.140555 17.454 < 2e-16 ***
## as.factor(DID)262 0.041071 0.162030 0.253 0.799899
## as.factor(DID)263 -0.621138 0.183416 -3.386 0.000708 ***
## as.factor(DID)264 -0.589965 0.178799 -3.300 0.000968 ***
## as.factor(DID)265 -1.036202 0.213848 -4.846 1.26e-06 ***
## as.factor(DID)266 -1.933466 0.300113 -6.442 1.18e-10 ***
## as.factor(DID)267 -1.109122 0.214611 -5.168 2.37e-07 ***
## as.factor(DID)268 -0.566712 0.214151 -2.646 0.008137 **
## as.factor(DID)269 -1.772714 0.299992 -5.909 3.44e-09 ***
## as.factor(DID)270 -0.666780 0.191847 -3.476 0.000510 ***
## as.factor(DID)271 -1.964836 0.330517 -5.945 2.77e-09 ***
## as.factor(DID)272 -1.835837 0.308805 -5.945 2.77e-09 ***
## as.factor(DID)273 -0.524383 0.197366 -2.657 0.007886 **
## as.factor(DID)274 -1.712992 0.360658 -4.750 2.04e-06 ***
## as.factor(DID)275 0.700111 0.155528 4.502 6.75e-06 ***
## as.factor(DID)276 -1.845706 0.283766 -6.504 7.80e-11 ***
## as.factor(DID)277 0.117407 0.230016 0.510 0.609749
## as.factor(DID)278 0.132079 0.149549 0.883 0.377136
## as.factor(DID)279 1.713579 0.144491 11.859 < 2e-16 ***
## as.factor(DID)280 0.005145 0.168870 0.030 0.975695
## as.factor(DID)281 -0.246341 0.167741 -1.469 0.141948
## as.factor(DID)282 0.485184 0.165041 2.940 0.003284 **
## as.factor(DID)284 0.539209 0.154745 3.484 0.000493 ***
## as.factor(DID)287 0.394145 0.163977 2.404 0.016232 *
## as.factor(DID)289 0.788105 0.148014 5.325 1.01e-07 ***
## as.factor(DID)290 1.026304 0.151500 6.774 1.25e-11 ***
## as.factor(DID)315 0.509505 0.183353 2.779 0.005456 **
## as.factor(DID)316 1.256011 0.149396 8.407 < 2e-16 ***
## as.factor(DID)318 1.658577 0.149388 11.103 < 2e-16 ***
## as.factor(DID)319 1.851007 0.142415 12.997 < 2e-16 ***
## as.factor(DID)320 1.465126 0.151500 9.671 < 2e-16 ***
## as.factor(C001)6 -0.905357 0.025404 -35.638 < 2e-16 ***
## as.factor(C001)7 -1.470798 0.027447 -53.588 < 2e-16 ***
## as.factor(C001)8 -1.638903 0.027676 -59.218 < 2e-16 ***
## as.factor(C001)9 -1.853320 0.031677 -58.507 < 2e-16 ***
## as.factor(C001)10 -1.791283 0.028801 -62.195 < 2e-16 ***
## as.factor(C001)11 -1.808296 0.036236 -49.903 < 2e-16 ***
## as.factor(C001)12 -1.682242 0.031559 -53.305 < 2e-16 ***
## as.factor(C001)13 -1.562901 0.033728 -46.338 < 2e-16 ***
## as.factor(C001)14 -1.490416 0.032505 -45.852 < 2e-16 ***
## as.factor(C001)15 -1.300463 0.035088 -37.063 < 2e-16 ***
## as.factor(C001)16 -1.235461 0.032697 -37.785 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175563 on 208249 degrees of freedom
## Residual deviance: 136852 on 208090 degrees of freedom
## AIC: 137172
##
## Number of Fisher Scoring iterations: 7## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 23.808, df = 8, p-value = 0.002468AIC
extractAIC(glm_child)## [1] 160.0 137171.9BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 160.0 138811.3effectiveness of explanatory variables
glm_child_null <- glm(C003_1_01~1, family = "binomial",
data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")multicolinearity
vif(glm_child)## GVIF Df GVIF^(1/(2*Df))
## n_children_in_household 1.170770 1 1.082021
## C002_01 1.035386 1 1.017539
## PR004_only_01 1.045240 1 1.022370
## PR009_only_01 1.198179 1 1.094614
## PR004_PR009_both_01 1.298763 1 1.139633
## as.factor(DID) 1.647567 143 1.001747
## as.factor(C001) 1.096843 11 1.004210Districts with Bigger Than 0.05
GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID)
glm_child <- glm(C003_1_01 ~
n_children_in_household +
C002_01 +
PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID),
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## PR004_only_01 + PR009_only_01 + PR004_PR009_both_01 + as.factor(DID),
## family = "binomial", data = child_ica_dummy %>% filter(C001 >=
## 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5765 -0.5979 -0.3583 -0.1691 3.5287
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.088711 0.130278 -23.709 < 2e-16 ***
## n_children_in_household 0.073889 0.004537 16.285 < 2e-16 ***
## C002_01 0.973996 0.013600 71.618 < 2e-16 ***
## PR004_only_01 -0.366037 0.038712 -9.455 < 2e-16 ***
## PR009_only_01 -0.495777 0.017224 -28.783 < 2e-16 ***
## PR004_PR009_both_01 -0.863551 0.021895 -39.441 < 2e-16 ***
## as.factor(DID)147 0.267551 0.161194 1.660 0.096953 .
## as.factor(DID)148 -0.745217 0.192233 -3.877 0.000106 ***
## as.factor(DID)149 0.221378 0.157797 1.403 0.160639
## as.factor(DID)150 0.158111 0.156256 1.012 0.311599
## as.factor(DID)151 -0.630034 0.196734 -3.202 0.001363 **
## as.factor(DID)152 -0.254627 0.194159 -1.311 0.189711
## as.factor(DID)153 0.582818 0.154947 3.761 0.000169 ***
## as.factor(DID)154 -0.004070 0.169745 -0.024 0.980870
## as.factor(DID)155 0.308074 0.152964 2.014 0.044006 *
## as.factor(DID)156 1.595035 0.162716 9.803 < 2e-16 ***
## as.factor(DID)157 -0.212763 0.182336 -1.167 0.243260
## as.factor(DID)158 -0.758062 0.194441 -3.899 9.67e-05 ***
## as.factor(DID)159 1.547324 0.151171 10.236 < 2e-16 ***
## as.factor(DID)160 0.494293 0.154313 3.203 0.001359 **
## as.factor(DID)161 1.236903 0.140397 8.810 < 2e-16 ***
## as.factor(DID)162 -0.255313 0.199635 -1.279 0.200933
## as.factor(DID)163 -0.188180 0.199159 -0.945 0.344723
## as.factor(DID)164 0.763991 0.165534 4.615 3.93e-06 ***
## as.factor(DID)165 0.720604 0.153284 4.701 2.59e-06 ***
## as.factor(DID)166 -0.402252 0.183436 -2.193 0.028316 *
## as.factor(DID)167 -1.022371 0.225607 -4.532 5.85e-06 ***
## as.factor(DID)169 1.860780 0.139912 13.300 < 2e-16 ***
## as.factor(DID)170 0.231021 0.164777 1.402 0.160908
## as.factor(DID)171 -0.094650 0.177401 -0.534 0.593660
## as.factor(DID)172 -0.074935 0.172033 -0.436 0.663138
## as.factor(DID)173 -0.128817 0.169806 -0.759 0.448084
## as.factor(DID)174 0.552311 0.159535 3.462 0.000536 ***
## as.factor(DID)175 1.254975 0.146069 8.592 < 2e-16 ***
## as.factor(DID)176 -1.396998 0.318036 -4.393 1.12e-05 ***
## as.factor(DID)177 0.320148 0.155937 2.053 0.040066 *
## as.factor(DID)178 -2.419262 0.376931 -6.418 1.38e-10 ***
## as.factor(DID)179 -0.430290 0.188198 -2.286 0.022233 *
## as.factor(DID)180 0.192095 0.162529 1.182 0.237239
## as.factor(DID)181 -1.535052 0.276232 -5.557 2.74e-08 ***
## as.factor(DID)182 1.540280 0.142358 10.820 < 2e-16 ***
## as.factor(DID)183 0.454729 0.157773 2.882 0.003950 **
## as.factor(DID)184 1.354138 0.142222 9.521 < 2e-16 ***
## as.factor(DID)185 -2.206682 0.289773 -7.615 2.63e-14 ***
## as.factor(DID)186 1.683653 0.141254 11.919 < 2e-16 ***
## as.factor(DID)187 1.612459 0.142726 11.298 < 2e-16 ***
## as.factor(DID)188 1.164003 0.145125 8.021 1.05e-15 ***
## as.factor(DID)189 0.150156 0.160658 0.935 0.349980
## as.factor(DID)190 1.051771 0.153881 6.835 8.20e-12 ***
## as.factor(DID)191 0.528752 0.161748 3.269 0.001079 **
## as.factor(DID)192 0.479501 0.160409 2.989 0.002797 **
## as.factor(DID)193 -0.851517 0.186012 -4.578 4.70e-06 ***
## as.factor(DID)194 1.630212 0.141321 11.536 < 2e-16 ***
## as.factor(DID)195 2.097849 0.141286 14.848 < 2e-16 ***
## as.factor(DID)196 -0.302192 0.198932 -1.519 0.128744
## as.factor(DID)197 1.423960 0.143141 9.948 < 2e-16 ***
## as.factor(DID)198 2.001490 0.140244 14.271 < 2e-16 ***
## as.factor(DID)199 -1.590277 0.227719 -6.983 2.88e-12 ***
## as.factor(DID)200 1.746889 0.143874 12.142 < 2e-16 ***
## as.factor(DID)202 1.103387 0.147963 7.457 8.84e-14 ***
## as.factor(DID)203 1.621913 0.139993 11.586 < 2e-16 ***
## as.factor(DID)204 0.560661 0.151963 3.689 0.000225 ***
## as.factor(DID)205 1.560604 0.140253 11.127 < 2e-16 ***
## as.factor(DID)206 1.269490 0.141246 8.988 < 2e-16 ***
## as.factor(DID)207 2.050115 0.139367 14.710 < 2e-16 ***
## as.factor(DID)208 1.146535 0.143548 7.987 1.38e-15 ***
## as.factor(DID)209 0.869728 0.156843 5.545 2.94e-08 ***
## as.factor(DID)210 1.625689 0.138828 11.710 < 2e-16 ***
## as.factor(DID)211 0.860828 0.156332 5.506 3.66e-08 ***
## as.factor(DID)212 1.293692 0.143754 8.999 < 2e-16 ***
## as.factor(DID)213 1.260507 0.141847 8.886 < 2e-16 ***
## as.factor(DID)214 1.849210 0.140470 13.164 < 2e-16 ***
## as.factor(DID)215 0.793419 0.142102 5.583 2.36e-08 ***
## as.factor(DID)216 1.764522 0.142493 12.383 < 2e-16 ***
## as.factor(DID)217 1.608667 0.141682 11.354 < 2e-16 ***
## as.factor(DID)218 1.664216 0.138699 11.999 < 2e-16 ***
## as.factor(DID)219 1.442222 0.143510 10.050 < 2e-16 ***
## as.factor(DID)220 1.896997 0.136653 13.882 < 2e-16 ***
## as.factor(DID)221 2.109676 0.140459 15.020 < 2e-16 ***
## as.factor(DID)222 1.570743 0.139188 11.285 < 2e-16 ***
## as.factor(DID)223 1.306498 0.152627 8.560 < 2e-16 ***
## as.factor(DID)224 1.881861 0.140437 13.400 < 2e-16 ***
## as.factor(DID)225 1.527166 0.143041 10.676 < 2e-16 ***
## as.factor(DID)226 1.538213 0.143424 10.725 < 2e-16 ***
## as.factor(DID)227 1.153281 0.140864 8.187 2.67e-16 ***
## as.factor(DID)228 2.351848 0.141252 16.650 < 2e-16 ***
## as.factor(DID)229 1.593976 0.143688 11.093 < 2e-16 ***
## as.factor(DID)230 2.008054 0.140239 14.319 < 2e-16 ***
## as.factor(DID)231 1.292720 0.140975 9.170 < 2e-16 ***
## as.factor(DID)232 0.303129 0.154774 1.959 0.050168 .
## as.factor(DID)233 2.201350 0.143631 15.326 < 2e-16 ***
## as.factor(DID)234 1.501111 0.148199 10.129 < 2e-16 ***
## as.factor(DID)235 0.113731 0.160103 0.710 0.477482
## as.factor(DID)236 0.707738 0.152881 4.629 3.67e-06 ***
## as.factor(DID)237 0.445267 0.157510 2.827 0.004700 **
## as.factor(DID)238 -2.169656 0.307083 -7.065 1.60e-12 ***
## as.factor(DID)239 -0.961190 0.204627 -4.697 2.64e-06 ***
## as.factor(DID)240 0.884368 0.150721 5.868 4.42e-09 ***
## as.factor(DID)241 1.496772 0.142546 10.500 < 2e-16 ***
## as.factor(DID)242 0.936450 0.149432 6.267 3.69e-10 ***
## as.factor(DID)243 -1.623716 0.378236 -4.293 1.76e-05 ***
## as.factor(DID)244 0.227218 0.171533 1.325 0.185295
## as.factor(DID)245 -0.440961 0.189964 -2.321 0.020271 *
## as.factor(DID)246 1.583942 0.143996 11.000 < 2e-16 ***
## as.factor(DID)247 1.405639 0.150138 9.362 < 2e-16 ***
## as.factor(DID)248 0.516925 0.147267 3.510 0.000448 ***
## as.factor(DID)249 -0.312794 0.190741 -1.640 0.101028
## as.factor(DID)250 0.623316 0.151335 4.119 3.81e-05 ***
## as.factor(DID)251 0.483757 0.157277 3.076 0.002099 **
## as.factor(DID)252 0.791984 0.150479 5.263 1.42e-07 ***
## as.factor(DID)253 -0.193737 0.185236 -1.046 0.295612
## as.factor(DID)254 1.815073 0.160570 11.304 < 2e-16 ***
## as.factor(DID)255 0.729138 0.160277 4.549 5.38e-06 ***
## as.factor(DID)256 -0.912317 0.193177 -4.723 2.33e-06 ***
## as.factor(DID)257 -0.459639 0.217414 -2.114 0.034506 *
## as.factor(DID)258 1.416724 0.142970 9.909 < 2e-16 ***
## as.factor(DID)259 -0.749764 0.210188 -3.567 0.000361 ***
## as.factor(DID)260 -0.299417 0.168243 -1.780 0.075130 .
## as.factor(DID)261 2.481736 0.137943 17.991 < 2e-16 ***
## as.factor(DID)262 0.138127 0.159118 0.868 0.385352
## as.factor(DID)263 -0.473653 0.180734 -2.621 0.008775 **
## as.factor(DID)264 -0.489033 0.175887 -2.780 0.005429 **
## as.factor(DID)265 -0.968277 0.211168 -4.585 4.53e-06 ***
## as.factor(DID)266 -1.890424 0.298239 -6.339 2.32e-10 ***
## as.factor(DID)267 -1.047534 0.212302 -4.934 8.05e-07 ***
## as.factor(DID)268 -0.535530 0.211514 -2.532 0.011345 *
## as.factor(DID)269 -1.667534 0.298043 -5.595 2.21e-08 ***
## as.factor(DID)270 -0.727408 0.189481 -3.839 0.000124 ***
## as.factor(DID)271 -1.956139 0.328997 -5.946 2.75e-09 ***
## as.factor(DID)272 -1.785517 0.307041 -5.815 6.05e-09 ***
## as.factor(DID)273 -0.436199 0.194478 -2.243 0.024902 *
## as.factor(DID)274 -1.722504 0.358977 -4.798 1.60e-06 ***
## as.factor(DID)275 0.852233 0.151876 5.611 2.01e-08 ***
## as.factor(DID)276 -1.911637 0.282324 -6.771 1.28e-11 ***
## as.factor(DID)277 0.176444 0.226610 0.779 0.436202
## as.factor(DID)278 0.353988 0.146447 2.417 0.015641 *
## as.factor(DID)279 1.810321 0.141533 12.791 < 2e-16 ***
## as.factor(DID)280 0.102586 0.165898 0.618 0.536333
## as.factor(DID)281 -0.004126 0.164396 -0.025 0.979978
## as.factor(DID)282 0.637281 0.161196 3.953 7.70e-05 ***
## as.factor(DID)284 0.632971 0.151597 4.175 2.98e-05 ***
## as.factor(DID)287 0.482346 0.160831 2.999 0.002708 **
## as.factor(DID)289 0.883251 0.145391 6.075 1.24e-09 ***
## as.factor(DID)290 1.130739 0.148042 7.638 2.21e-14 ***
## as.factor(DID)315 0.463907 0.180093 2.576 0.009997 **
## as.factor(DID)316 1.239599 0.146404 8.467 < 2e-16 ***
## as.factor(DID)318 1.712416 0.146038 11.726 < 2e-16 ***
## as.factor(DID)319 1.860087 0.139617 13.323 < 2e-16 ***
## as.factor(DID)320 1.486619 0.148114 10.037 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 175563 on 208249 degrees of freedom
## Residual deviance: 144784 on 208101 degrees of freedom
## AIC: 145082
##
## Number of Fisher Scoring iterations: 7## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 96.524, df = 8, p-value < 2.2e-16AIC
extractAIC(glm_child)## [1] 149.0 145081.9BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 149.0 146608.6effectiveness of explanatory variables
glm_child_null <- glm(C003_1_01 ~ 1, family = "binomial",
data = child_ica_dummy %>% filter(C001 >= 5))
anova(glm_child_null, glm_child, test = "Chisq")multicolinearity
vif(glm_child)## GVIF Df GVIF^(1/(2*Df))
## n_children_in_household 1.156358 1 1.075341
## C002_01 1.029088 1 1.014440
## PR004_only_01 1.044432 1 1.021975
## PR009_only_01 1.192847 1 1.092175
## PR004_PR009_both_01 1.283116 1 1.132747
## as.factor(DID) 1.544326 143 1.001521GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + as.factor(H006) + as.factor(DID)
glm_child <- glm(C003_1_01 ~
n_children_in_household +
C002_01 +
as.factor(H006) + as.factor(DID),
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## as.factor(H006) + as.factor(DID), family = "binomial", data = child_ica_dummy %>%
## filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6199 -0.5981 -0.3660 -0.1768 3.4949
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.721591 0.134213 -27.729 < 2e-16 ***
## n_children_in_household 0.086422 0.004525 19.101 < 2e-16 ***
## C002_01 0.959674 0.013615 70.488 < 2e-16 ***
## as.factor(H006)0 0.320610 0.015132 21.188 < 2e-16 ***
## as.factor(DID)147 0.373850 0.164530 2.272 0.023072 *
## as.factor(DID)148 -0.687998 0.195054 -3.527 0.000420 ***
## as.factor(DID)149 0.311001 0.161194 1.929 0.053686 .
## as.factor(DID)150 0.255117 0.159758 1.597 0.110288
## as.factor(DID)151 -0.645914 0.200582 -3.220 0.001281 **
## as.factor(DID)152 -0.228736 0.196910 -1.162 0.245386
## as.factor(DID)153 0.708896 0.158507 4.472 7.74e-06 ***
## as.factor(DID)154 -0.013057 0.173296 -0.075 0.939939
## as.factor(DID)155 0.404614 0.156765 2.581 0.009851 **
## as.factor(DID)156 1.485768 0.166714 8.912 < 2e-16 ***
## as.factor(DID)157 -0.106205 0.185234 -0.573 0.566404
## as.factor(DID)158 -0.678217 0.197140 -3.440 0.000581 ***
## as.factor(DID)159 1.507692 0.154714 9.745 < 2e-16 ***
## as.factor(DID)160 0.610088 0.157789 3.866 0.000110 ***
## as.factor(DID)161 1.240051 0.144499 8.582 < 2e-16 ***
## as.factor(DID)162 -0.340061 0.202365 -1.680 0.092873 .
## as.factor(DID)163 -0.230785 0.201753 -1.144 0.252665
## as.factor(DID)164 0.512950 0.172153 2.980 0.002886 **
## as.factor(DID)165 0.776188 0.157144 4.939 7.84e-07 ***
## as.factor(DID)166 -0.235220 0.186355 -1.262 0.206873
## as.factor(DID)167 -0.964749 0.227868 -4.234 2.30e-05 ***
## as.factor(DID)169 2.175578 0.143735 15.136 < 2e-16 ***
## as.factor(DID)170 0.308901 0.168054 1.838 0.066047 .
## as.factor(DID)171 -0.061331 0.181487 -0.338 0.735412
## as.factor(DID)172 0.034519 0.175134 0.197 0.843748
## as.factor(DID)173 0.072958 0.172862 0.422 0.672982
## as.factor(DID)174 0.672010 0.162819 4.127 3.67e-05 ***
## as.factor(DID)175 1.358325 0.149810 9.067 < 2e-16 ***
## as.factor(DID)176 -1.604798 0.319693 -5.020 5.17e-07 ***
## as.factor(DID)177 0.355006 0.159880 2.220 0.026388 *
## as.factor(DID)178 -2.556271 0.378461 -6.754 1.43e-11 ***
## as.factor(DID)179 -0.379835 0.191004 -1.989 0.046743 *
## as.factor(DID)180 0.410080 0.166009 2.470 0.013503 *
## as.factor(DID)181 -1.611553 0.278145 -5.794 6.88e-09 ***
## as.factor(DID)182 1.672374 0.146282 11.433 < 2e-16 ***
## as.factor(DID)183 0.632183 0.161119 3.924 8.72e-05 ***
## as.factor(DID)184 1.549160 0.146027 10.609 < 2e-16 ***
## as.factor(DID)185 -1.939592 0.291595 -6.652 2.90e-11 ***
## as.factor(DID)186 1.845831 0.145226 12.710 < 2e-16 ***
## as.factor(DID)187 1.809977 0.146568 12.349 < 2e-16 ***
## as.factor(DID)188 1.290672 0.148983 8.663 < 2e-16 ***
## as.factor(DID)189 0.285890 0.164870 1.734 0.082912 .
## as.factor(DID)190 1.137408 0.157498 7.222 5.13e-13 ***
## as.factor(DID)191 0.706168 0.164957 4.281 1.86e-05 ***
## as.factor(DID)192 0.432183 0.164289 2.631 0.008523 **
## as.factor(DID)193 -0.532890 0.190478 -2.798 0.005148 **
## as.factor(DID)194 1.824593 0.145176 12.568 < 2e-16 ***
## as.factor(DID)195 2.212446 0.145354 15.221 < 2e-16 ***
## as.factor(DID)196 -0.310973 0.201625 -1.542 0.122992
## as.factor(DID)197 1.549356 0.147030 10.538 < 2e-16 ***
## as.factor(DID)198 2.263674 0.144074 15.712 < 2e-16 ***
## as.factor(DID)199 -1.275408 0.230122 -5.542 2.99e-08 ***
## as.factor(DID)200 1.974083 0.147714 13.364 < 2e-16 ***
## as.factor(DID)202 1.294832 0.151600 8.541 < 2e-16 ***
## as.factor(DID)203 1.682469 0.143998 11.684 < 2e-16 ***
## as.factor(DID)204 0.810130 0.155538 5.209 1.90e-07 ***
## as.factor(DID)205 1.684444 0.144143 11.686 < 2e-16 ***
## as.factor(DID)206 1.550575 0.146961 10.551 < 2e-16 ***
## as.factor(DID)207 2.530467 0.143200 17.671 < 2e-16 ***
## as.factor(DID)208 1.471103 0.147205 9.994 < 2e-16 ***
## as.factor(DID)209 1.074390 0.160971 6.674 2.48e-11 ***
## as.factor(DID)210 2.107355 0.142714 14.766 < 2e-16 ***
## as.factor(DID)211 1.053142 0.159837 6.589 4.43e-11 ***
## as.factor(DID)212 1.577562 0.147757 10.677 < 2e-16 ***
## as.factor(DID)213 1.484127 0.145787 10.180 < 2e-16 ***
## as.factor(DID)214 2.206505 0.144248 15.297 < 2e-16 ***
## as.factor(DID)215 1.254658 0.145937 8.597 < 2e-16 ***
## as.factor(DID)216 1.962306 0.146336 13.410 < 2e-16 ***
## as.factor(DID)217 2.053721 0.145840 14.082 < 2e-16 ***
## as.factor(DID)218 1.727461 0.142972 12.082 < 2e-16 ***
## as.factor(DID)219 1.756289 0.147591 11.900 < 2e-16 ***
## as.factor(DID)220 2.152511 0.140709 15.298 < 2e-16 ***
## as.factor(DID)221 2.427731 0.144775 16.769 < 2e-16 ***
## as.factor(DID)222 1.803915 0.143144 12.602 < 2e-16 ***
## as.factor(DID)223 1.569790 0.156253 10.046 < 2e-16 ***
## as.factor(DID)224 2.078743 0.144380 14.398 < 2e-16 ***
## as.factor(DID)225 1.592242 0.147191 10.817 < 2e-16 ***
## as.factor(DID)226 1.896638 0.147327 12.874 < 2e-16 ***
## as.factor(DID)227 1.631808 0.144732 11.275 < 2e-16 ***
## as.factor(DID)228 2.842546 0.146252 19.436 < 2e-16 ***
## as.factor(DID)229 1.875530 0.147839 12.686 < 2e-16 ***
## as.factor(DID)230 2.324956 0.144045 16.140 < 2e-16 ***
## as.factor(DID)231 1.605670 0.144866 11.084 < 2e-16 ***
## as.factor(DID)232 0.425844 0.159006 2.678 0.007403 **
## as.factor(DID)233 2.550016 0.147287 17.313 < 2e-16 ***
## as.factor(DID)234 1.777228 0.152827 11.629 < 2e-16 ***
## as.factor(DID)235 0.281495 0.163638 1.720 0.085392 .
## as.factor(DID)236 0.815723 0.157297 5.186 2.15e-07 ***
## as.factor(DID)237 0.621455 0.160877 3.863 0.000112 ***
## as.factor(DID)238 -1.989754 0.308784 -6.444 1.16e-10 ***
## as.factor(DID)239 -0.813200 0.207155 -3.926 8.65e-05 ***
## as.factor(DID)240 0.991898 0.154429 6.423 1.34e-10 ***
## as.factor(DID)241 1.737582 0.146439 11.866 < 2e-16 ***
## as.factor(DID)242 1.223177 0.152933 7.998 1.26e-15 ***
## as.factor(DID)243 -1.783268 0.379548 -4.698 2.62e-06 ***
## as.factor(DID)244 0.126585 0.174664 0.725 0.468617
## as.factor(DID)245 -0.274783 0.192729 -1.426 0.153941
## as.factor(DID)246 1.642066 0.147825 11.108 < 2e-16 ***
## as.factor(DID)247 1.739968 0.153749 11.317 < 2e-16 ***
## as.factor(DID)248 0.850994 0.151006 5.635 1.75e-08 ***
## as.factor(DID)249 -0.030823 0.193493 -0.159 0.873435
## as.factor(DID)250 0.841096 0.154950 5.428 5.69e-08 ***
## as.factor(DID)251 0.659748 0.160661 4.106 4.02e-05 ***
## as.factor(DID)252 1.016486 0.154010 6.600 4.11e-11 ***
## as.factor(DID)253 -0.205772 0.188125 -1.094 0.274040
## as.factor(DID)254 2.174153 0.164308 13.232 < 2e-16 ***
## as.factor(DID)255 0.785862 0.163524 4.806 1.54e-06 ***
## as.factor(DID)256 -0.856276 0.201658 -4.246 2.17e-05 ***
## as.factor(DID)257 -0.577349 0.221811 -2.603 0.009244 **
## as.factor(DID)258 1.427925 0.146683 9.735 < 2e-16 ***
## as.factor(DID)259 -0.380349 0.214338 -1.775 0.075976 .
## as.factor(DID)260 -0.282441 0.171883 -1.643 0.100339
## as.factor(DID)261 2.748235 0.142027 19.350 < 2e-16 ***
## as.factor(DID)262 0.326642 0.162485 2.010 0.044401 *
## as.factor(DID)263 -0.329459 0.184333 -1.787 0.073889 .
## as.factor(DID)264 -0.391696 0.179051 -2.188 0.028697 *
## as.factor(DID)265 -0.795216 0.213648 -3.722 0.000198 ***
## as.factor(DID)266 -1.844336 0.299942 -6.149 7.80e-10 ***
## as.factor(DID)267 -1.116769 0.214833 -5.198 2.01e-07 ***
## as.factor(DID)268 -0.649688 0.215653 -3.013 0.002590 **
## as.factor(DID)269 -2.013211 0.308815 -6.519 7.07e-11 ***
## as.factor(DID)270 -0.592870 0.192305 -3.083 0.002049 **
## as.factor(DID)271 -2.157673 0.330584 -6.527 6.72e-11 ***
## as.factor(DID)272 -2.031639 0.308746 -6.580 4.70e-11 ***
## as.factor(DID)273 -0.637853 0.198236 -3.218 0.001292 **
## as.factor(DID)274 -1.932309 0.360480 -5.360 8.31e-08 ***
## as.factor(DID)275 0.891903 0.155347 5.741 9.39e-09 ***
## as.factor(DID)276 -2.056423 0.284225 -7.235 4.65e-13 ***
## as.factor(DID)277 0.086716 0.231224 0.375 0.707637
## as.factor(DID)278 0.537303 0.150240 3.576 0.000348 ***
## as.factor(DID)279 2.026590 0.145572 13.922 < 2e-16 ***
## as.factor(DID)280 0.165453 0.169033 0.979 0.327666
## as.factor(DID)281 0.015086 0.167710 0.090 0.928326
## as.factor(DID)282 0.614022 0.164727 3.728 0.000193 ***
## as.factor(DID)284 0.840550 0.155481 5.406 6.44e-08 ***
## as.factor(DID)287 0.483301 0.166195 2.908 0.003637 **
## as.factor(DID)289 0.960635 0.149138 6.441 1.19e-10 ***
## as.factor(DID)290 1.352580 0.151762 8.913 < 2e-16 ***
## as.factor(DID)315 0.419144 0.182905 2.292 0.021929 *
## as.factor(DID)316 1.451766 0.150023 9.677 < 2e-16 ***
## as.factor(DID)318 2.127577 0.149675 14.215 < 2e-16 ***
## as.factor(DID)319 2.147538 0.143462 14.969 < 2e-16 ***
## as.factor(DID)320 1.618863 0.151783 10.666 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 173392 on 205841 degrees of freedom
## Residual deviance: 144415 on 205695 degrees of freedom
## (2408 observations deleted due to missingness)
## AIC: 144709
##
## Number of Fisher Scoring iterations: 7## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 90.634, df = 8, p-value = 3.331e-16GLM Age >= 5. GLM C003_1_01 ~ n_children_in_household + C002_01 + as.factor(H004) + as.factor(DID)
glm_child <- glm(C003_1_01 ~
n_children_in_household +
C002_01 +
as.factor(H004) + as.factor(DID),
family = "binomial", data = child_ica_dummy %>% filter(C001 >= 5))
ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary = TRUE, point = TRUE)##
## Call:
## glm(formula = C003_1_01 ~ n_children_in_household + C002_01 +
## as.factor(H004) + as.factor(DID), family = "binomial", data = child_ica_dummy %>%
## filter(C001 >= 5))
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7811 -0.5924 -0.3638 -0.1775 3.4743
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.674284 0.135274 -27.162 < 2e-16 ***
## n_children_in_household 0.082739 0.004537 18.236 < 2e-16 ***
## C002_01 0.962566 0.013642 70.558 < 2e-16 ***
## as.factor(H004)0 0.585211 0.019488 30.029 < 2e-16 ***
## as.factor(DID)147 0.369970 0.165457 2.236 0.025348 *
## as.factor(DID)148 -0.661002 0.196811 -3.359 0.000783 ***
## as.factor(DID)149 0.302150 0.162119 1.864 0.062356 .
## as.factor(DID)150 0.348154 0.160594 2.168 0.030165 *
## as.factor(DID)151 -0.616992 0.200161 -3.082 0.002053 **
## as.factor(DID)152 -0.244668 0.197664 -1.238 0.215791
## as.factor(DID)153 0.712930 0.159347 4.474 7.67e-06 ***
## as.factor(DID)154 -0.026813 0.174504 -0.154 0.877882
## as.factor(DID)155 0.445712 0.157907 2.823 0.004763 **
## as.factor(DID)156 1.454625 0.167529 8.683 < 2e-16 ***
## as.factor(DID)157 -0.123912 0.185997 -0.666 0.505280
## as.factor(DID)158 -0.659186 0.197880 -3.331 0.000865 ***
## as.factor(DID)159 1.527356 0.155688 9.810 < 2e-16 ***
## as.factor(DID)160 0.590042 0.159326 3.703 0.000213 ***
## as.factor(DID)161 1.305486 0.145482 8.973 < 2e-16 ***
## as.factor(DID)162 -0.394190 0.205592 -1.917 0.055194 .
## as.factor(DID)163 -0.218770 0.202365 -1.081 0.279668
## as.factor(DID)164 0.443789 0.175903 2.523 0.011639 *
## as.factor(DID)165 0.724727 0.158008 4.587 4.50e-06 ***
## as.factor(DID)166 -0.234487 0.187135 -1.253 0.210194
## as.factor(DID)167 -1.002185 0.228499 -4.386 1.15e-05 ***
## as.factor(DID)169 1.914266 0.145230 13.181 < 2e-16 ***
## as.factor(DID)170 0.305500 0.169275 1.805 0.071112 .
## as.factor(DID)171 0.008159 0.181215 0.045 0.964089
## as.factor(DID)172 0.023677 0.175962 0.135 0.892960
## as.factor(DID)173 0.049177 0.173720 0.283 0.777115
## as.factor(DID)174 0.638238 0.163821 3.896 9.78e-05 ***
## as.factor(DID)175 1.327014 0.150792 8.800 < 2e-16 ***
## as.factor(DID)176 -1.587351 0.320114 -4.959 7.10e-07 ***
## as.factor(DID)177 0.318071 0.160486 1.982 0.047487 *
## as.factor(DID)178 -2.524114 0.378846 -6.663 2.69e-11 ***
## as.factor(DID)179 -0.336789 0.191707 -1.757 0.078954 .
## as.factor(DID)180 0.386345 0.166872 2.315 0.020601 *
## as.factor(DID)181 -1.566604 0.278646 -5.622 1.89e-08 ***
## as.factor(DID)182 1.572877 0.147362 10.674 < 2e-16 ***
## as.factor(DID)183 0.547969 0.162730 3.367 0.000759 ***
## as.factor(DID)184 1.491184 0.147253 10.127 < 2e-16 ***
## as.factor(DID)185 -1.942884 0.292094 -6.652 2.90e-11 ***
## as.factor(DID)186 1.546274 0.146936 10.523 < 2e-16 ***
## as.factor(DID)187 1.707959 0.147791 11.557 < 2e-16 ***
## as.factor(DID)188 1.338757 0.149816 8.936 < 2e-16 ***
## as.factor(DID)189 0.304896 0.165085 1.847 0.064760 .
## as.factor(DID)190 1.236196 0.158110 7.819 5.34e-15 ***
## as.factor(DID)191 0.730086 0.165890 4.401 1.08e-05 ***
## as.factor(DID)192 0.522978 0.165131 3.167 0.001540 **
## as.factor(DID)193 -0.539562 0.191229 -2.822 0.004779 **
## as.factor(DID)194 1.868904 0.146231 12.780 < 2e-16 ***
## as.factor(DID)195 2.087176 0.146458 14.251 < 2e-16 ***
## as.factor(DID)196 -0.213436 0.202245 -1.055 0.291273
## as.factor(DID)197 1.594481 0.147935 10.778 < 2e-16 ***
## as.factor(DID)198 2.183850 0.145189 15.041 < 2e-16 ***
## as.factor(DID)199 -1.437922 0.231008 -6.225 4.83e-10 ***
## as.factor(DID)200 1.888885 0.148763 12.697 < 2e-16 ***
## as.factor(DID)202 1.185039 0.152863 7.752 9.02e-15 ***
## as.factor(DID)203 1.711379 0.144984 11.804 < 2e-16 ***
## as.factor(DID)204 0.712449 0.156867 4.542 5.58e-06 ***
## as.factor(DID)205 1.692428 0.145115 11.663 < 2e-16 ***
## as.factor(DID)206 1.288826 0.148589 8.674 < 2e-16 ***
## as.factor(DID)207 2.538284 0.144338 17.586 < 2e-16 ***
## as.factor(DID)208 1.483712 0.148214 10.011 < 2e-16 ***
## as.factor(DID)209 0.780425 0.162493 4.803 1.56e-06 ***
## as.factor(DID)210 2.085609 0.143912 14.492 < 2e-16 ***
## as.factor(DID)211 1.065433 0.160860 6.623 3.51e-11 ***
## as.factor(DID)212 1.503263 0.148792 10.103 < 2e-16 ***
## as.factor(DID)213 1.425913 0.146956 9.703 < 2e-16 ***
## as.factor(DID)214 2.246881 0.145209 15.473 < 2e-16 ***
## as.factor(DID)215 1.209923 0.146917 8.235 < 2e-16 ***
## as.factor(DID)216 1.992841 0.147446 13.516 < 2e-16 ***
## as.factor(DID)217 1.974725 0.146541 13.476 < 2e-16 ***
## as.factor(DID)218 1.558420 0.144335 10.797 < 2e-16 ***
## as.factor(DID)219 1.733668 0.148800 11.651 < 2e-16 ***
## as.factor(DID)220 2.268518 0.141647 16.015 < 2e-16 ***
## as.factor(DID)221 2.366030 0.145894 16.217 < 2e-16 ***
## as.factor(DID)222 1.855652 0.144094 12.878 < 2e-16 ***
## as.factor(DID)223 1.318116 0.157834 8.351 < 2e-16 ***
## as.factor(DID)224 2.149526 0.145269 14.797 < 2e-16 ***
## as.factor(DID)225 1.478411 0.148462 9.958 < 2e-16 ***
## as.factor(DID)226 1.607432 0.148898 10.796 < 2e-16 ***
## as.factor(DID)227 1.663849 0.145610 11.427 < 2e-16 ***
## as.factor(DID)228 2.769627 0.146804 18.866 < 2e-16 ***
## as.factor(DID)229 1.680662 0.149313 11.256 < 2e-16 ***
## as.factor(DID)230 2.319949 0.145041 15.995 < 2e-16 ***
## as.factor(DID)231 1.677526 0.145764 11.509 < 2e-16 ***
## as.factor(DID)232 0.235262 0.160262 1.468 0.142109
## as.factor(DID)233 2.486440 0.148435 16.751 < 2e-16 ***
## as.factor(DID)234 1.478804 0.153939 9.606 < 2e-16 ***
## as.factor(DID)235 0.231326 0.164436 1.407 0.159491
## as.factor(DID)236 0.749374 0.158141 4.739 2.15e-06 ***
## as.factor(DID)237 0.598388 0.162327 3.686 0.000228 ***
## as.factor(DID)238 -1.974512 0.309256 -6.385 1.72e-10 ***
## as.factor(DID)239 -0.779025 0.207857 -3.748 0.000178 ***
## as.factor(DID)240 0.977693 0.155282 6.296 3.05e-10 ***
## as.factor(DID)241 1.429672 0.148115 9.652 < 2e-16 ***
## as.factor(DID)242 1.147270 0.155268 7.389 1.48e-13 ***
## as.factor(DID)243 -1.808804 0.379924 -4.761 1.93e-06 ***
## as.factor(DID)244 0.154513 0.175891 0.878 0.379695
## as.factor(DID)245 -0.294640 0.193545 -1.522 0.127926
## as.factor(DID)246 1.634134 0.148793 10.983 < 2e-16 ***
## as.factor(DID)247 1.699415 0.154817 10.977 < 2e-16 ***
## as.factor(DID)248 0.689497 0.152174 4.531 5.87e-06 ***
## as.factor(DID)249 -0.087501 0.194440 -0.450 0.652699
## as.factor(DID)250 0.813255 0.157793 5.154 2.55e-07 ***
## as.factor(DID)251 0.538697 0.162533 3.314 0.000918 ***
## as.factor(DID)252 1.031739 0.154952 6.658 2.77e-11 ***
## as.factor(DID)253 -0.155311 0.188890 -0.822 0.410947
## as.factor(DID)254 1.976693 0.165921 11.913 < 2e-16 ***
## as.factor(DID)255 0.774559 0.164424 4.711 2.47e-06 ***
## as.factor(DID)256 -0.792758 0.201138 -3.941 8.10e-05 ***
## as.factor(DID)257 -0.515138 0.220372 -2.338 0.019409 *
## as.factor(DID)258 1.438077 0.147761 9.732 < 2e-16 ***
## as.factor(DID)259 -0.776860 0.215533 -3.604 0.000313 ***
## as.factor(DID)260 -0.180933 0.172356 -1.050 0.293829
## as.factor(DID)261 2.821725 0.143016 19.730 < 2e-16 ***
## as.factor(DID)262 0.348119 0.163394 2.131 0.033126 *
## as.factor(DID)263 -0.260958 0.184449 -1.415 0.157127
## as.factor(DID)264 -0.277613 0.179761 -1.544 0.122505
## as.factor(DID)265 -0.764156 0.214315 -3.566 0.000363 ***
## as.factor(DID)266 -1.854871 0.300441 -6.174 6.67e-10 ***
## as.factor(DID)267 -1.144558 0.215498 -5.311 1.09e-07 ***
## as.factor(DID)268 -0.618150 0.214597 -2.881 0.003970 **
## as.factor(DID)269 -1.919097 0.300248 -6.392 1.64e-10 ***
## as.factor(DID)270 -0.717553 0.194149 -3.696 0.000219 ***
## as.factor(DID)271 -2.184105 0.331025 -6.598 4.17e-11 ***
## as.factor(DID)272 -2.009316 0.309241 -6.498 8.16e-11 ***
## as.factor(DID)273 -0.634134 0.198950 -3.187 0.001436 **
## as.factor(DID)274 -1.910088 0.360839 -5.293 1.20e-07 ***
## as.factor(DID)275 0.864599 0.156152 5.537 3.08e-08 ***
## as.factor(DID)276 -2.058385 0.284731 -7.229 4.86e-13 ***
## as.factor(DID)277 0.099404 0.229441 0.433 0.664836
## as.factor(DID)278 0.560424 0.151118 3.709 0.000208 ***
## as.factor(DID)279 1.930761 0.146781 13.154 < 2e-16 ***
## as.factor(DID)280 0.211374 0.169877 1.244 0.213396
## as.factor(DID)281 0.089322 0.168748 0.529 0.596585
## as.factor(DID)282 0.826767 0.165343 5.000 5.72e-07 ***
## as.factor(DID)284 0.934917 0.156276 5.982 2.20e-09 ***
## as.factor(DID)287 0.675127 0.166211 4.062 4.87e-05 ***
## as.factor(DID)289 1.065284 0.149978 7.103 1.22e-12 ***
## as.factor(DID)290 1.044960 0.153164 6.823 8.95e-12 ***
## as.factor(DID)315 0.441218 0.183732 2.401 0.016332 *
## as.factor(DID)316 1.442042 0.151232 9.535 < 2e-16 ***
## as.factor(DID)318 1.983840 0.151014 13.137 < 2e-16 ***
## as.factor(DID)319 2.115026 0.144608 14.626 < 2e-16 ***
## as.factor(DID)320 1.642749 0.152956 10.740 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 173453 on 205782 degrees of freedom
## Residual deviance: 144007 on 205636 degrees of freedom
## (2467 observations deleted due to missingness)
## AIC: 144301
##
## Number of Fisher Scoring iterations: 7## Warning in ggPredict(glm_child, se = TRUE, colorAsFactor = TRUE, show.summary =
## TRUE, : maximum three independent variables are allowed## NULLHosmer-Lemeshow
hoslem.test(x = glm_child$y, y = fitted(glm_child))##
## Hosmer and Lemeshow goodness of fit (GOF) test
##
## data: glm_child$y, fitted(glm_child)
## X-squared = 80.463, df = 8, p-value = 3.941e-14memo
fit the model
glm_child <- glm(C003_01~ C001 + C002_01 + PR004_01, family = "binomial", data = child_ica_dummy)
summary(glm_child)##
## Call:
## glm(formula = C003_01 ~ C001 + C002_01 + PR004_01, family = "binomial",
## data = child_ica_dummy)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4716 -1.0691 0.6141 0.8525 1.4442
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.567479 0.012767 -44.45 <2e-16 ***
## C001 0.174066 0.001340 129.90 <2e-16 ***
## C002_01 -0.562952 0.009369 -60.09 <2e-16 ***
## PR004_01 0.788430 0.010620 74.24 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 299726 on 245746 degrees of freedom
## Residual deviance: 271929 on 245743 degrees of freedom
## AIC: 271937
##
## Number of Fisher Scoring iterations: 4exponential transformation
exp(glm_child$coefficients)## (Intercept) C001 C002_01 PR004_01
## 0.5669529 1.1901343 0.5695255 2.1999390confidence interval (intercept and coefficient)
confint(glm_child, level = 0.95)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) -0.5925094 -0.5424624
## C001 0.1714426 0.1766953
## C002_01 -0.5813180 -0.5445920
## PR004_01 0.7676346 0.8092649exponential transformation of confidence interval (odds ratio of intercept and coefficient)
exp(confint(glm_child, level = 0.95))## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 0.5529380 0.5813151
## C001 1.1870160 1.1932675
## C002_01 0.5591609 0.5800784
## PR004_01 2.1546637 2.2462561AIC
extractAIC(glm_child)## [1] 4.0 271937.4BIC
extractAIC(glm_child, k = log(nrow(glm_child$data)))## [1] 4.0 271979.1effectiveness of explanatory variables
glm_child_null <- glm(C003_01~1, family = "binomial", data = child_ica_dummy)
anova(glm_child_null, glm_child, test = "Chisq")variables selection
step(glm_child_null, direction = "both",
scope = (~ C001 + C002_01 + PR004_01 + PR009_01))## Start: AIC=299727.8
## C003_01 ~ 1
##
## Df Deviance AIC
## + C001 1 281139 281143
## + PR009_01 1 293719 293723
## + PR004_01 1 295038 295042
## + C002_01 1 295943 295947
## <none> 299726 299728
##
## Step: AIC=281142.5
## C003_01 ~ C001
##
## Df Deviance AIC
## + PR009_01 1 274422 274428
## + PR004_01 1 275560 275566
## + C002_01 1 277803 277809
## <none> 281139 281143
## - C001 1 299726 299728
##
## Step: AIC=274428
## C003_01 ~ C001 + PR009_01
##
## Df Deviance AIC
## + C002_01 1 270765 270773
## + PR004_01 1 272824 272832
## <none> 274422 274428
## - PR009_01 1 281139 281143
## - C001 1 293719 293723
##
## Step: AIC=270773.3
## C003_01 ~ C001 + PR009_01 + C002_01
##
## Df Deviance AIC
## + PR004_01 1 269072 269082
## <none> 270765 270773
## - C002_01 1 274422 274428
## - PR009_01 1 277803 277809
## - C001 1 289624 289630
##
## Step: AIC=269081.6
## C003_01 ~ C001 + PR009_01 + C002_01 + PR004_01
##
## Df Deviance AIC
## <none> 269072 269082
## - PR004_01 1 270765 270773
## - PR009_01 1 271929 271937
## - C002_01 1 272824 272832
## - C001 1 288269 288277##
## Call: glm(formula = C003_01 ~ C001 + PR009_01 + C002_01 + PR004_01,
## family = "binomial", data = child_ica_dummy)
##
## Coefficients:
## (Intercept) C001 PR009_01 C002_01 PR004_01
## -0.7815 0.1760 0.5677 -0.5762 0.4923
##
## Degrees of Freedom: 245746 Total (i.e. Null); 245742 Residual
## Null Deviance: 299700
## Residual Deviance: 269100 AIC: 269100multicolinearity
vif(glm_child)## C001 C002_01 PR004_01
## 1.010465 1.004067 1.013630Further Analysis
dists_not_fit
Sample Size
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID, C002_01) %>%
summarize(n = n()) %>%
ggplot(aes(factor(DID), n, fill = factor(C002_01))) +
geom_col(position = position_dodge()) +
scale_fill_manual(values = c("darkseagreen", "burlywood")) +
geom_text(aes(label = n), check_overlap = TRUE, size = 3, vjust = -1) +
theme(axis.text.x = element_text(angle = 85)) +
ggtitle("Sample Size in Each District")## `summarise()` regrouping output by 'DID' (override with `.groups` argument)
Average Enrollment Rate
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID) %>%
summarize(avg_enrollment = mean(C003 == 3))## `summarise()` ungrouping output (override with `.groups` argument)Age, Enrollment
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID, C002_01, C001) %>%
mutate(avg = mean(C003 == 3)) %>%
ggplot(aes(C001, avg, color = factor(C002_01))) +
geom_line() +
facet_wrap(.~DID)
Parets Education
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID) %>%
summarize(parents_edu_rate = mean(PR004_PR009_01 == 1),
mother = mean(PR004 == -1),
father = mean(PR009 == -1))## `summarise()` ungrouping output (override with `.groups` argument)child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID) %>%
mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
mother = mean(PR004 == -1),
father = mean(PR009 == -1)) %>%
ggplot(aes(factor(DID), parents_edu_rate, color = factor(RID))) +
geom_point() +
annotate("rect", xmin = 0.5, xmax = 28.5, ymin = 0.5, ymax = 1,
alpha = .1, fill = "deepskyblue") +
ggtitle("What Percent of Couples of Parents Have Educational Experience?")
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID) %>%
mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
mother = mean(PR004 == -1),
father = mean(PR009 == -1)) %>%
ggplot(aes(factor(DID), fill = factor(PR004_PR009_01))) +
geom_histogram(position = position_stack(), stat = "count") +
ggtitle("Mothers or Fathers Ever Been To School?")## Warning: Ignoring unknown parameters: binwidth, bins, pad
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID) %>%
mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
mother = mean(PR004 == -1),
father = mean(PR009 == -1)) %>%
ggplot(aes(factor(DID), fill = factor(PR004_01))) +
geom_histogram(position = position_stack(), stat = "count") +
ggtitle("Mothers Ever Been To School?")## Warning: Ignoring unknown parameters: binwidth, bins, pad
child_ica_dummy %>%
filter(DID %in% dists_not_fit) %>%
group_by(DID) %>%
mutate(parents_edu_rate = mean(PR004_PR009_01 == 1),
mother = mean(PR004 == -1),
father = mean(PR009 == -1)) %>%
ggplot(aes(factor(DID), fill = factor(PR009_01))) +
geom_histogram(position = position_stack(), stat = "count") +
ggtitle("Fathers Ever Been To School?")## Warning: Ignoring unknown parameters: binwidth, bins, pad
boxplot
child_ica_dummy %>%
filter(DID == dists_not_fit) %>%
ggplot(aes(factor(DID), fill = factor(C003))) +
geom_histogram(position = position_stack(), stat = "count") +
xlab("Districts") +
ggtitle("Education Status by Distrists Whose Data Don't Fit The Model Well")## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません## Warning: Ignoring unknown parameters: binwidth, bins, pad
Paid-Tuition?
child_ica_dummy %>%
filter(DID == dists_not_fit) %>%
ggplot(aes(factor(DID), fill = factor(C008a))) +
geom_histogram(stat = "count", position = position_stack()) +
ggtitle("Receiving Paid-Tuition?")## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません## Warning: Ignoring unknown parameters: binwidth, bins, pad
Electricity
child_ica_dummy %>%
filter(DID == dists_not_fit) %>%
ggplot(aes(factor(DID), fill = factor(H004))) +
geom_histogram(stat = "count", position = position_stack()) +
ggtitle("Electricity Available in House?")## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません## Warning: Ignoring unknown parameters: binwidth, bins, pad
TV
child_ica_dummy %>%
filter(DID == dists_not_fit) %>%
ggplot(aes(factor(DID), fill = factor(H005))) +
geom_histogram(stat = "count", position = position_stack()) +
ggtitle("TV in House?")## Warning in DID == dists_not_fit: 長いオブジェクトの長さが短いオブジェクトの長さ
## の倍数になっていません## Warning: Ignoring unknown parameters: binwidth, bins, pad
DID == 199
child_ica_dummy %>%
filter(DID == 199) %>%
select(DNAME)Institution Type
child_ica_dummy %>%
filter(DID == 199) %>%
ggplot(aes(factor(C006))) +
geom_bar() +
geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
ggtitle("School Type", subtitle = "1:Gov., 2:Private, 3:Madrasah, 4:Other")
Paid-Tuition
child_ica_dummy %>%
filter(DID == 199) %>%
ggplot(aes(factor(C008a))) +
geom_bar() +
geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
ggtitle("Receiving Paid-Tuition?")
Literacy
child_ica_dummy %>%
filter(DID == 199) %>%
ggplot(aes(factor(C001), fill = factor(C010))) +
geom_bar(position = position_stack(), stat = "count") +
ggtitle("Literacy by Age", subtitle = "1:Beginner/Nothing, 2:Letters, 3:Words, 4:Sentences, 5:Story")
testing
child_ica_dummy %>%
group_by(H004) %>%
summarize(avg = mean(C003 == 3))## `summarise()` ungrouping output (override with `.groups` argument)Age and Number of Children in Each Household
child_ica_dummy %>%
filter(C001 >= 5) %>%
ggplot(aes(factor(C001), fill = factor(n_children_in_household))) +
geom_bar(stat = "count", position = position_stack()) +
ggtitle("Correlation Between Age and Number of Children in Each Household?")
child_ica_dummy %>%
filter(C001 >= 5) %>%
ggplot(aes(factor(C001), fill = factor(n_children_in_household))) +
geom_bar(stat = "count", position = "fill") +
ggtitle("Correlation Between Age and Number of Children in Each Household?", subtitle = "Proportional Bar Plot")